BitBullet:Lessons: Binary Classification¶

Most machine learning workflows look like this: load data, throw it at a model, get a number, ship it. BitBullet is built for the teams who know that's not enough.

In this tutorial we build a complete, production-grade binary classification pipeline — from raw data to a serialised, fully-evaluated, SHAP-explained model — using nothing but bitbullet and standard Python. Every design decision mirrors what you'd do in a real production system.

Dataset: E-Commerce Shipping Data — 11,000 shipments, 10 features.
Task: Predict whether a shipment will arrive on schedule (Reached.on.Time_Y.N = 1).

---

What You Will Build¶

Step	Component	What BitBullet Handles For You
1	Feature profiling	generate_feature_stats — full audit in one call
2	Hold-out test split	Stratified 80/20 split before any preprocessing
3	Transform pipeline	Fit on train only, serialise, reload, apply to test
4	Hyperparameter search	Optuna TPE + Stratified K-Fold + early stopping
5	Cross-model benchmark	One config change, four architectures
6	Test-set evaluation	ModelEvaluator + evaluate_classification — ROC AUC, PR AUC, Kappa, confusion matrix, JSON-ready report numbers
7	Model serialisation	ModelSerializer + ModelMetadata — full provenance
8	Explainability	SHAP computed inside the trainer — zero extra code
9	Formal report	TrainingReportGenerator — one call
10	Inference	Load pipeline + model from disk, predict on new records

In [1]:

# =============================================================================
# Section 1: Environment & Imports
# =============================================================================
import sys
import os
import warnings
from datetime import datetime

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, precision_recall_curve

warnings.filterwarnings('ignore')

# ── BitBullet core ────────────────────────────────────────────────────────────
# When running this notebook from bitbullet/lessons in a local clone, prefer the local SDK source.
sdk_root = os.path.abspath("..")
if sdk_root not in sys.path:
    sys.path.insert(0, sdk_root)

import bitbullet
print(f"bitbullet  : v{getattr(bitbullet, '__version__', 'dev')}")

from bitbullet.transform import TransformPipeline
from bitbullet.transform.eda import generate_feature_stats, plot_pipeline_transformations
from bitbullet.train import TrainConfig, OptunaTrainer, TrainingReportGenerator
from bitbullet.model import ModelMetadata, ModelSerializer

# ── BitBullet Evaluate — comprehensive model evaluation ──────────────────────
from bitbullet.evaluate import EvaluationConfig, ModelEvaluator, evaluate_classification

print("All imports successful.")

# ============================================================================= # Section 1: Environment & Imports # ============================================================================= import sys import os import warnings from datetime import datetime import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.metrics import roc_curve, precision_recall_curve warnings.filterwarnings('ignore') # ── BitBullet core ──────────────────────────────────────────────────────────── # When running this notebook from bitbullet/lessons in a local clone, prefer the local SDK source. sdk_root = os.path.abspath("..") if sdk_root not in sys.path: sys.path.insert(0, sdk_root) import bitbullet print(f"bitbullet : v{getattr(bitbullet, '__version__', 'dev')}") from bitbullet.transform import TransformPipeline from bitbullet.transform.eda import generate_feature_stats, plot_pipeline_transformations from bitbullet.train import TrainConfig, OptunaTrainer, TrainingReportGenerator from bitbullet.model import ModelMetadata, ModelSerializer # ── BitBullet Evaluate — comprehensive model evaluation ────────────────────── from bitbullet.evaluate import EvaluationConfig, ModelEvaluator, evaluate_classification print("All imports successful.")

bitbullet  : vdev
All imports successful.

2. Load the Dataset¶

Each row represents a single shipment. Features describe the warehouse origin, shipping mode, customer behaviour, and product characteristics. The binary target Reached.on.Time_Y.N records whether the parcel arrived on schedule.

Dataset: E-Commerce Shipping Dataset
Source: Kaggle — prachi13/customer-analytics
File: e-commerce-shipping-data.csv

Before running this cell: download e-commerce-shipping-data.csv from the link above and place it in the same directory as this notebook. If you store it elsewhere, update data_path in the code cell below to match your chosen location.

In [ ]:

# Update this path if you stored the file in a different location.
data_path = "e-commerce-shipping-data.csv"

df = pd.read_csv(data_path)
print(f"Dataset loaded — Shape: {df.shape}")

target_col = 'Reached.on.Time_Y.N'
X = df.drop(columns=[target_col, 'ID'])
y = df[target_col]

print(f"Features : {X.shape[1]}")
print(f"Target   : {y.value_counts().to_dict()}  (1 = on time, 0 = delayed)")
display(df.head())

# Update this path if you stored the file in a different location. data_path = "e-commerce-shipping-data.csv" df = pd.read_csv(data_path) print(f"Dataset loaded — Shape: {df.shape}") target_col = 'Reached.on.Time_Y.N' X = df.drop(columns=[target_col, 'ID']) y = df[target_col] print(f"Features : {X.shape[1]}") print(f"Target : {y.value_counts().to_dict()} (1 = on time, 0 = delayed)") display(df.head())

Dataset loaded — Shape: (10999, 12)
Features : 10
Target   : {1: 6563, 0: 4436}  (1 = on time, 0 = delayed)

	ID	Warehouse_block	Mode_of_Shipment	Customer_care_calls	Customer_rating	Cost_of_the_Product	Prior_purchases	Product_importance	Gender	Discount_offered	Weight_in_gms	Reached.on.Time_Y.N
0	1	D	Flight	4	2	177	3	low	F	44	1233	1
1	2	F	Flight	4	5	216	2	low	M	59	3088	1
2	3	A	Flight	2	2	183	4	low	M	48	3374	1
3	4	B	Flight	3	3	176	4	medium	M	10	1177	1
4	5	C	Flight	2	2	184	3	medium	F	46	2484	1

3. Exploratory Feature Analysis¶

Before touching a model we need to understand our data. generate_feature_stats produces a comprehensive feature-level audit in a single call — detecting missing values, skewness, kurtosis, and cardinality across all columns simultaneously.

In [3]:

stats_df, numerical_cols, categorical_cols = generate_feature_stats(X)

print(f"Numerical   ({len(numerical_cols)}): {numerical_cols}")
print(f"Categorical ({len(categorical_cols)}): {categorical_cols}")
print("-" * 60)
display(stats_df)

balance = y.value_counts(normalize=True)
print(f"\nTarget balance — On Time: {balance[1]:.1%}  |  Delayed: {balance[0]:.1%}")

stats_df, numerical_cols, categorical_cols = generate_feature_stats(X) print(f"Numerical ({len(numerical_cols)}): {numerical_cols}") print(f"Categorical ({len(categorical_cols)}): {categorical_cols}") print("-" * 60) display(stats_df) balance = y.value_counts(normalize=True) print(f"\nTarget balance — On Time: {balance[1]:.1%} | Delayed: {balance[0]:.1%}")

Numerical   (6): ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms']
Categorical (4): ['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender']
------------------------------------------------------------

	dtype	missing_count	missing_percent	unique_values	zero_count	negative_count	mean	std	skewness	kurtosis	mean_percentile	min	25%	50%	75%	max	top	freq
Warehouse_block	object	0	0.0	5	0	0	-	-	-	-	-	-	-	-	-	-	F	3666
Mode_of_Shipment	object	0	0.0	3	0	0	-	-	-	-	-	-	-	-	-	-	Ship	7462
Customer_care_calls	int64	0	0.0	6	0	0	4.054459	1.14149	0.391926	-0.308995	67.387944	2.0	3.0	4.0	5.0	7.0	-	-
Customer_rating	int64	0	0.0	5	0	0	2.990545	1.413603	0.00436	-1.295654	40.003637	1.0	2.0	3.0	4.0	5.0	-	-
Cost_of_the_Product	int64	0	0.0	215	0	0	210.196836	48.063272	-0.157117	-0.97216	48.049823	96.0	169.0	214.0	251.0	310.0	-	-
Prior_purchases	int64	0	0.0	8	0	0	3.567597	1.52286	1.681897	4.006342	59.587235	2.0	3.0	3.0	4.0	10.0	-	-
Product_importance	object	0	0.0	3	0	0	-	-	-	-	-	-	-	-	-	-	low	5297
Gender	object	0	0.0	2	0	0	-	-	-	-	-	-	-	-	-	-	F	5545
Discount_offered	int64	0	0.0	65	0	0	13.373216	16.205527	1.798929	2.000586	77.407037	1.0	4.0	7.0	10.0	65.0	-	-
Weight_in_gms	int64	0	0.0	4034	0	0	3634.016729	1635.377251	-0.249747	-1.447671	42.722066	1001.0	1839.5	4149.0	5050.0	7846.0	-	-

Target balance — On Time: 59.7%  |  Delayed: 40.3%

Observations from the audit:

• Discount_offered: skewness = 1.80, kurtosis = 2.00 — heavily right-skewed. A log1p transform will compress the long tail.
• Prior_purchases: skewness = 1.68 — boxcox normalisation followed by standard_scale is the correct treatment.
• Weight_in_gms: kurtosis = −1.45 indicating a bimodal distribution — standard_scale preserves shape while keeping values manageable.
• All four categoricals are low-cardinality — ideal for one-hot encoding.
• No missing values anywhere — we can proceed without imputation.

4. Establishing the Hold-Out Test Set¶

The cardinal rule of model evaluation: split your data before any preprocessing.

Fitting a transform on the full dataset and then splitting leaks test-set information into your training process — e.g., a StandardScaler whose mean was computed from test rows, or a BoxCox lambda tuned on the full distribution. The result is an optimistically biased evaluation score that overstates real-world performance.

We use stratified splitting (stratify=y) to preserve the exact class ratio in both partitions. This matters most for imbalanced targets.

In [4]:

X_train, X_test, y_train, y_test = train_test_split(
    X, y,
    test_size=0.20,
    random_state=42,
    stratify=y
)

print(f"Training : {X_train.shape[0]:,} samples  (on-time rate: {y_train.mean():.1%})")
print(f"Test     : {X_test.shape[0]:,}  samples  (on-time rate: {y_test.mean():.1%})")
print("\nThe test set is now sealed. It will not be seen by any fitting step.")

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.20, random_state=42, stratify=y ) print(f"Training : {X_train.shape[0]:,} samples (on-time rate: {y_train.mean():.1%})") print(f"Test : {X_test.shape[0]:,} samples (on-time rate: {y_test.mean():.1%})") print("\nThe test set is now sealed. It will not be seen by any fitting step.")

Training : 8,799 samples  (on-time rate: 59.7%)
Test     : 2,200  samples  (on-time rate: 59.7%)

The test set is now sealed. It will not be seen by any fitting step.

5. Feature Engineering with TransformPipeline¶

5.1 Designing the Pipeline¶

With bitbullet.transform you specify what to transform and how — nothing more. The pipeline manages fitting, chaining, state, and serialisation automatically.

Key capability on display here: bitbullet supports sequential chaining of multiple transforms on the same column in a single fluent API call. The pipeline executes them in order — boxcox normalises the distribution, then standard_scale brings it to unit variance. No separate pipeline stages or custom wrappers needed.

In [5]:

pipeline = TransformPipeline(name="shipping_delay_pipeline")

# Compress the heavy right tail in discount offers
pipeline.add(
    transformer_type="numerical",
    method="log1p",
    columns=['Discount_offered']
)

# Normalise the skewed purchase-count distribution, then scale to unit variance
# Note: bitbullet supports chaining multiple transforms on the same column
pipeline.add(
    transformer_type="numerical",
    method="boxcox",
    columns=['Prior_purchases']
).add(
    transformer_type="numerical",
    method="standard_scale",
    columns=['Prior_purchases']
)

# Standardise remaining numerics
pipeline.add(
    transformer_type="numerical",
    method="standard_scale",
    columns=['Weight_in_gms', 'Cost_of_the_Product']
)

# Encode all categoricals
pipeline.add(
    transformer_type="categorical",
    method="onehot_encode",
    columns=['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender']
)

print(pipeline)
print()
for step in pipeline.list_steps():
    print(f"  [{step['type']:12s}] {step['method']:20s} → {step['columns']}")

pipeline = TransformPipeline(name="shipping_delay_pipeline") # Compress the heavy right tail in discount offers pipeline.add( transformer_type="numerical", method="log1p", columns=['Discount_offered'] ) # Normalise the skewed purchase-count distribution, then scale to unit variance # Note: bitbullet supports chaining multiple transforms on the same column pipeline.add( transformer_type="numerical", method="boxcox", columns=['Prior_purchases'] ).add( transformer_type="numerical", method="standard_scale", columns=['Prior_purchases'] ) # Standardise remaining numerics pipeline.add( transformer_type="numerical", method="standard_scale", columns=['Weight_in_gms', 'Cost_of_the_Product'] ) # Encode all categoricals pipeline.add( transformer_type="categorical", method="onehot_encode", columns=['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender'] ) print(pipeline) print() for step in pipeline.list_steps(): print(f" [{step['type']:12s}] {step['method']:20s} → {step['columns']}")

TransformPipeline(name='shipping_delay_pipeline', steps=5, status=not fitted)

  [numerical   ] log1p                → ['Discount_offered']
  [numerical   ] boxcox               → ['Prior_purchases']
  [numerical   ] standard_scale       → ['Prior_purchases']
  [numerical   ] standard_scale       → ['Weight_in_gms', 'Cost_of_the_Product']
  [categorical ] onehot_encode        → ['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender']

5.2 Fit on Training Data Only¶

We call fit_transform() exclusively on X_train. Every learned parameter — the Box-Cox lambda, the scaler mean and variance, the one-hot encoder category vocabulary — is derived from the training partition only.

In [6]:

X_train_transformed = pipeline.fit_transform(X_train)

print(f"Training features : {X_train.shape[1]} raw → {X_train_transformed.shape[1]} post-transform")
print(f"Training samples  : {X_train_transformed.shape[0]:,}")
print(f"Pipeline fitted   : {pipeline.is_fitted}")

X_train_transformed = pipeline.fit_transform(X_train) print(f"Training features : {X_train.shape[1]} raw → {X_train_transformed.shape[1]} post-transform") print(f"Training samples : {X_train_transformed.shape[0]:,}") print(f"Pipeline fitted : {pipeline.is_fitted}")

Training features : 10 raw → 19 post-transform
Training samples  : 8,799
Pipeline fitted   : True

5.3 Serialise the Fitted Pipeline — Reproducibility Guaranteed¶

A trained model is only as good as the preprocessing that feeds it. By serialising the fitted pipeline — with all its learned parameters intact — we guarantee that every future inference applies identical transforms.

We save the pipeline, reload it from disk, and apply it to the test set. In production, this reload step would live in your inference service, completely decoupled from the training environment.

In [7]:

pipeline_path = "pipeline_binary.pkl"
pipeline.save(pipeline_path)
print(f"Pipeline serialised → {pipeline_path}")

# Reload from disk — simulating a production inference environment
loaded_pipeline = TransformPipeline.load(pipeline_path)
print(f"Pipeline reloaded  — fitted: {loaded_pipeline.is_fitted}  |  steps: {len(loaded_pipeline)}")

# Apply learned parameters to the test set
X_test_transformed = loaded_pipeline.transform(X_test)

print(f"\nTest set  : {X_test.shape} → {X_test_transformed.shape}")
print(f"Column alignment: {list(X_train_transformed.columns) == list(X_test_transformed.columns)}")

pipeline_path = "pipeline_binary.pkl" pipeline.save(pipeline_path) print(f"Pipeline serialised → {pipeline_path}") # Reload from disk — simulating a production inference environment loaded_pipeline = TransformPipeline.load(pipeline_path) print(f"Pipeline reloaded — fitted: {loaded_pipeline.is_fitted} | steps: {len(loaded_pipeline)}") # Apply learned parameters to the test set X_test_transformed = loaded_pipeline.transform(X_test) print(f"\nTest set : {X_test.shape} → {X_test_transformed.shape}") print(f"Column alignment: {list(X_train_transformed.columns) == list(X_test_transformed.columns)}")

Pipeline serialised → pipeline_binary.pkl
Pipeline reloaded  — fitted: True  |  steps: 5

Test set  : (2200, 10) → (2200, 19)
Column alignment: True

5.4 Visualise the Transformation Results¶

Because the TransformPipeline tracks full state of what it transformed, plot_pipeline_transformations can automatically render before-and-after distributions for every modified feature — including decoding one-hot columns back to their original categorical form. Use the dropdown to explore each transformation.

In [8]:

plot_pipeline_transformations(pipeline, X_train, X_train_transformed)

plot_pipeline_transformations(pipeline, X_train, X_train_transformed)

interactive(children=(Dropdown(description='feature', options=('Cost_of_the_Product', 'Discount_offered', 'Gen…

6. Model Training¶

6.1 Train the Champion Model¶

Training a production-grade gradient-boosted model normally means crafting a custom Optuna study, wiring up stratified cross-validation, managing early stopping inside each fold, and separately computing feature importance and SHAP values. bitbullet.train compresses all of that into a single config declaration.

Parameter	Value	Why
n_trials	50	Balanced speed vs. search quality (use 200+ in production)
cv_folds	5	Robust CV estimate with acceptable variance
optimize_threshold	True	Youden's J finds the optimal decision boundary — not naïvely 0.5
generate_shap	True	SHAP computed inside the trainer — zero extra code
save_feature_importance	True	Native tree importance stored automatically in training state

In [9]:

lgbm_config = TrainConfig(
    name="shipping_delay_lgbm",
    model_type="lgbm",
    task="binary_classification",
    optimization_metric="roc_auc",
    optuna_sampler="tpe",
    n_trials=5,
    cv_folds=5,
    optimize_threshold=True,
    threshold_optimization_method="youden",
    save_feature_importance=True,
    generate_shap=True,
    shap_n_samples=500,
    shap_n_background=100,
    verbose=True,
    optuna_show_progress=False,
    random_state=42
)

lgbm_trainer = OptunaTrainer(config=lgbm_config)
lgbm_trainer.fit(X_train_transformed, y_train)
lgbm_state = lgbm_trainer.state

lgbm_config = TrainConfig( name="shipping_delay_lgbm", model_type="lgbm", task="binary_classification", optimization_metric="roc_auc", optuna_sampler="tpe", n_trials=5, cv_folds=5, optimize_threshold=True, threshold_optimization_method="youden", save_feature_importance=True, generate_shap=True, shap_n_samples=500, shap_n_background=100, verbose=True, optuna_show_progress=False, random_state=42 ) lgbm_trainer = OptunaTrainer(config=lgbm_config) lgbm_trainer.fit(X_train_transformed, y_train) lgbm_state = lgbm_trainer.state

[I 2026-05-05 06:11:58,028] A new study created in memory with name: shipping_delay_lgbm

======================================================================
BitBullet Train - shipping_delay_lgbm
======================================================================

Training samples: 8799
Features: 19
Class distribution: {1: 5250, 0: 3549}

Starting hyperparameter optimization (optuna)...
Trials: 5, CV Folds: 5

[I 2026-05-05 06:12:03,465] Trial 1 finished with value: 0.741780080955881 and parameters: {'num_leaves': 101, 'max_depth': 5, 'min_child_samples': 12, 'lambda_l1': 5.676984647356448, 'lambda_l2': 1.6838303342028776e-05, 'min_gain_to_split': 0.20599930872563244, 'feature_fraction': 0.5236829320328156, 'bagging_fraction': 0.8068332236316801, 'bagging_freq': 6, 'learning_rate': 0.06669632833052522, 'max_bin': 159}. Best is trial 1 with value: 0.741780080955881.
[I 2026-05-05 06:12:05,334] Trial 4 finished with value: 0.7425801371463383 and parameters: {'num_leaves': 125, 'max_depth': 7, 'min_child_samples': 53, 'lambda_l1': 6.590011235194716e-07, 'lambda_l2': 7.259104745433034e-07, 'min_gain_to_split': 0.16842722147088796, 'feature_fraction': 0.9460292540594877, 'bagging_fraction': 0.5649193881943484, 'bagging_freq': 2, 'learning_rate': 0.09153075700296597, 'max_bin': 63}. Best is trial 4 with value: 0.7425801371463383.
[I 2026-05-05 06:12:28,781] Trial 0 finished with value: 0.7406091170435873 and parameters: {'num_leaves': 175, 'max_depth': 6, 'min_child_samples': 24, 'lambda_l1': 0.7746983115991543, 'lambda_l2': 0.00020178628857019844, 'min_gain_to_split': 0.20025153454243405, 'feature_fraction': 0.7139297149143418, 'bagging_fraction': 0.6041193157217839, 'bagging_freq': 1, 'learning_rate': 0.009249970223670419, 'max_bin': 159}. Best is trial 4 with value: 0.7425801371463383.
[I 2026-05-05 06:12:48,642] Trial 2 finished with value: 0.739534439547487 and parameters: {'num_leaves': 291, 'max_depth': 9, 'min_child_samples': 20, 'lambda_l1': 6.439181537353084e-08, 'lambda_l2': 1.7249089840159149e-06, 'min_gain_to_split': 0.9675323487351434, 'feature_fraction': 0.6963879821641755, 'bagging_fraction': 0.5692611727350684, 'bagging_freq': 1, 'learning_rate': 0.0066258025932934535, 'max_bin': 127}. Best is trial 4 with value: 0.7425801371463383.
[I 2026-05-05 06:13:46,839] Trial 3 finished with value: 0.7346561475103559 and parameters: {'num_leaves': 60, 'max_depth': 12, 'min_child_samples': 5, 'lambda_l1': 0.05655632782389785, 'lambda_l2': 1.533417822319273e-06, 'min_gain_to_split': 0.10072405223000014, 'feature_fraction': 0.7061100679113412, 'bagging_fraction': 0.8481301643691066, 'bagging_freq': 5, 'learning_rate': 0.008252909857164148, 'max_bin': 223}. Best is trial 4 with value: 0.7425801371463383.

======================================================================
Optuna Optimization Summary
======================================================================
Number of finished trials: 5
Best trial: 4
Best value: 0.742580

Best hyperparameters:
  num_leaves: 125
  max_depth: 7
  min_child_samples: 53
  lambda_l1: 0.000001
  lambda_l2: 0.000001
  min_gain_to_split: 0.168427
  feature_fraction: 0.946029
  bagging_fraction: 0.564919
  bagging_freq: 2
  learning_rate: 0.091531
  max_bin: 63

Additional attributes:
  cv_scores: [0.7602481556002683, 0.735627095908786, 0.7370811535881958, 0.7252193158953723, 0.754724964739069]
  cv_std: 0.01295868381030559
  mean_training_loss: 0.43233581979803226
  mean_validation_loss: 0.5164681044108509
  best_iteration: 48
======================================================================


Optimization complete! Best score: 0.7426
Time: 108.8s

Training final model with best parameters...
[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877
[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07
[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796
[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07
[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877
[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07
[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796
[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07
[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
[LightGBM] [Info] Number of positive: 5250, number of negative: 3549
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001649 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 228
[LightGBM] [Info] Number of data points in the train set: 8799, number of used features: 19
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.596659 -> initscore=0.391562
[LightGBM] [Info] Start training from score 0.391562
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

Optimizing classification threshold...
[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877
[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07
[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796
[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07
[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
Optimal threshold: 0.498

Generating SHAP explanations...
SHAP values computed for 500 samples
Top 5 SHAP features:
  Discount_offered: 1.4766
  Weight_in_gms: 0.6442
  Prior_purchases: 0.1425
  Cost_of_the_Product: 0.1280
  Customer_rating: 0.0455

======================================================================
Training Complete!
======================================================================
=== Training Summary ===
Model: LGBMClassifier
Best Score: 0.7426
CV Score: 0.0000 ± 0.0000
Features: 19/19
Optimal Threshold: 0.498
Training Time: 111.3s
Optimization Time: 108.8s

Best Hyperparameters:
  num_leaves: 125
  max_depth: 7
  min_child_samples: 53
  lambda_l1: 0.0000
  lambda_l2: 0.0000
  min_gain_to_split: 0.1684
  feature_fraction: 0.9460
  bagging_fraction: 0.5649
  bagging_freq: 2
  learning_rate: 0.0915
  max_bin: 63
  n_estimators: 48
======================================================================

6.2 Inspecting the Rich Training State¶

After fit() completes, trainer.state is a TrainState object that captures everything produced by the training run: the trained model, hyperparameters, threshold optimisation, feature importance, SHAP values, and timing — all in one place.

In [10]:

print(lgbm_state.summary())

# Per-fold CV scores are stored in the best Optuna trial's user attributes
if lgbm_state.study:
    best_trial  = lgbm_state.study.best_trial
    fold_scores = best_trial.user_attrs.get('cv_scores', [])
    fold_std    = best_trial.user_attrs.get('cv_std', 0.0)
    if fold_scores:
        print(f"\nBest trial — per-fold AUC : {[round(s, 4) for s in fold_scores]}")
        print(f"Mean ± Std                : {np.mean(fold_scores):.4f} ± {fold_std:.4f}")

print(f"\nTraining state snapshot:")
print(f"  Model               : {type(lgbm_state.model).__name__}")
print(f"  Optimal threshold   : {lgbm_state.optimal_threshold:.4f}")
print(f"  Feature importance  : {'Available' if lgbm_state.feature_importance is not None else 'Not computed'}")
print(f"  SHAP values         : {'Available' if lgbm_state.shap_values is not None else 'Not computed'}")
print(f"  Training time       : {lgbm_state.training_time_seconds:.1f}s")

print(lgbm_state.summary()) # Per-fold CV scores are stored in the best Optuna trial's user attributes if lgbm_state.study: best_trial = lgbm_state.study.best_trial fold_scores = best_trial.user_attrs.get('cv_scores', []) fold_std = best_trial.user_attrs.get('cv_std', 0.0) if fold_scores: print(f"\nBest trial — per-fold AUC : {[round(s, 4) for s in fold_scores]}") print(f"Mean ± Std : {np.mean(fold_scores):.4f} ± {fold_std:.4f}") print(f"\nTraining state snapshot:") print(f" Model : {type(lgbm_state.model).__name__}") print(f" Optimal threshold : {lgbm_state.optimal_threshold:.4f}") print(f" Feature importance : {'Available' if lgbm_state.feature_importance is not None else 'Not computed'}") print(f" SHAP values : {'Available' if lgbm_state.shap_values is not None else 'Not computed'}") print(f" Training time : {lgbm_state.training_time_seconds:.1f}s")

=== Training Summary ===
Model: LGBMClassifier
Best Score: 0.7426
CV Score: 0.0000 ± 0.0000
Features: 19/19
Optimal Threshold: 0.498
Training Time: 111.3s
Optimization Time: 108.8s

Best Hyperparameters:
  num_leaves: 125
  max_depth: 7
  min_child_samples: 53
  lambda_l1: 0.0000
  lambda_l2: 0.0000
  min_gain_to_split: 0.1684
  feature_fraction: 0.9460
  bagging_fraction: 0.5649
  bagging_freq: 2
  learning_rate: 0.0915
  max_bin: 63
  n_estimators: 48

Best trial — per-fold AUC : [0.7602, 0.7356, 0.7371, 0.7252, 0.7547]
Mean ± Std                : 0.7426 ± 0.0130

Training state snapshot:
  Model               : LGBMClassifierWrapper
  Optimal threshold   : 0.4984
  Feature importance  : Available
  SHAP values         : Available
  Training time       : 111.3s

7. Cross-Model Benchmarking¶

One of bitbullet.train's most powerful capabilities is architecture-agnostic training. Because every model shares the same TrainConfig interface, switching from LightGBM to XGBoost, Random Forest, or Logistic Regression is a single string change — no bespoke API knowledge required.

We use n_trials=20 here for benchmark speed. For production decisions, run at least 100 trials per architecture.

In [11]:

architectures = ['xgb', 'random_forest', 'logistic_regression']

# Seed the leaderboard with the LGBM model already trained above
model_states = {'lgbm': lgbm_state}

for arch in architectures:
    print(f"\n{'─' * 50}  {arch.upper()}")

    config = TrainConfig(
        name=f"shipping_delay_{arch}",
        model_type=arch,
        task="binary_classification",
        optimization_metric="roc_auc",
        n_trials=3,
        cv_folds=3,
        optimize_threshold=True,
        verbose=False,
        optuna_show_progress=False,
        random_state=42
    )

    trainer = OptunaTrainer(config=config)
    trainer.fit(X_train_transformed, y_train)
    model_states[arch] = trainer.state
    print(f"  CV AUC: {trainer.state.best_score:.4f}  |  Threshold: {trainer.state.optimal_threshold:.3f}")

print("\nAll architectures trained!")

architectures = ['xgb', 'random_forest', 'logistic_regression'] # Seed the leaderboard with the LGBM model already trained above model_states = {'lgbm': lgbm_state} for arch in architectures: print(f"\n{'─' * 50} {arch.upper()}") config = TrainConfig( name=f"shipping_delay_{arch}", model_type=arch, task="binary_classification", optimization_metric="roc_auc", n_trials=3, cv_folds=3, optimize_threshold=True, verbose=False, optuna_show_progress=False, random_state=42 ) trainer = OptunaTrainer(config=config) trainer.fit(X_train_transformed, y_train) model_states[arch] = trainer.state print(f" CV AUC: {trainer.state.best_score:.4f} | Threshold: {trainer.state.optimal_threshold:.3f}") print("\nAll architectures trained!")

[I 2026-05-05 06:14:12,299] A new study created in memory with name: shipping_delay_xgb

──────────────────────────────────────────────────  XGB
[0]	validation_0-logloss:0.66719	validation_1-logloss:0.66690
[0]	validation_0-logloss:0.67410	validation_1-logloss:0.67420
[0]	validation_0-logloss:0.63842	validation_1-logloss:0.64121
[1]	validation_0-logloss:0.66056	validation_1-logloss:0.66029
[1]	validation_0-logloss:0.66883	validation_1-logloss:0.66901
[2]	validation_0-logloss:0.65406	validation_1-logloss:0.65358
[1]	validation_0-logloss:0.60192	validation_1-logloss:0.60931
[2]	validation_0-logloss:0.66445	validation_1-logloss:0.66456
[3]	validation_0-logloss:0.64858	validation_1-logloss:0.64802
[2]	validation_0-logloss:0.59515	validation_1-logloss:0.60936[3]	validation_0-logloss:0.66434	validation_1-logloss:0.66445

[4]	validation_0-logloss:0.64229	validation_1-logloss:0.64187
[5]	validation_0-logloss:0.63683	validation_1-logloss:0.63626
[4]	validation_0-logloss:0.66004	validation_1-logloss:0.66010
[3]	validation_0-logloss:0.57419	validation_1-logloss:0.59117
[6]	validation_0-logloss:0.63154	validation_1-logloss:0.63101
[5]	validation_0-logloss:0.65978	validation_1-logloss:0.65997
[4]	validation_0-logloss:0.55410	validation_1-logloss:0.57368
[7]	validation_0-logloss:0.62629	validation_1-logloss:0.62567
[6]	validation_0-logloss:0.65501	validation_1-logloss:0.65507
[5]	validation_0-logloss:0.53692	validation_1-logloss:0.55929
[8]	validation_0-logloss:0.62143	validation_1-logloss:0.62086
[7]	validation_0-logloss:0.65120	validation_1-logloss:0.65129
[9]	validation_0-logloss:0.61658	validation_1-logloss:0.61595
[8]	validation_0-logloss:0.64666	validation_1-logloss:0.64672
[6]	validation_0-logloss:0.52251	validation_1-logloss:0.54921
[10]	validation_0-logloss:0.61231	validation_1-logloss:0.61153
[9]	validation_0-logloss:0.64257	validation_1-logloss:0.64255
[7]	validation_0-logloss:0.51166	validation_1-logloss:0.54116[11]	validation_0-logloss:0.60818	validation_1-logloss:0.60733

[10]	validation_0-logloss:0.63861	validation_1-logloss:0.63851
[12]	validation_0-logloss:0.60466	validation_1-logloss:0.60379
[11]	validation_0-logloss:0.63462	validation_1-logloss:0.63439[8]	validation_0-logloss:0.50305	validation_1-logloss:0.53420

[13]	validation_0-logloss:0.60069	validation_1-logloss:0.59968
[12]	validation_0-logloss:0.63134	validation_1-logloss:0.63101
[14]	validation_0-logloss:0.59718	validation_1-logloss:0.59609
[9]	validation_0-logloss:0.49579	validation_1-logloss:0.52955
[13]	validation_0-logloss:0.62748	validation_1-logloss:0.62721
[15]	validation_0-logloss:0.59371	validation_1-logloss:0.59249
[16]	validation_0-logloss:0.59034	validation_1-logloss:0.58916[14]	validation_0-logloss:0.62432	validation_1-logloss:0.62408

[10]	validation_0-logloss:0.48951	validation_1-logloss:0.52608
[17]	validation_0-logloss:0.58744	validation_1-logloss:0.58629
[15]	validation_0-logloss:0.62097	validation_1-logloss:0.62075
[11]	validation_0-logloss:0.48479	validation_1-logloss:0.52306
[18]	validation_0-logloss:0.58711	validation_1-logloss:0.58610
[16]	validation_0-logloss:0.61802	validation_1-logloss:0.61781
[19]	validation_0-logloss:0.58410	validation_1-logloss:0.58303
[12]	validation_0-logloss:0.47839	validation_1-logloss:0.51987
[17]	validation_0-logloss:0.61515	validation_1-logloss:0.61491
[20]	validation_0-logloss:0.58379	validation_1-logloss:0.58286
[21]	validation_0-logloss:0.58061	validation_1-logloss:0.57959
[18]	validation_0-logloss:0.61495	validation_1-logloss:0.61481
[13]	validation_0-logloss:0.47376	validation_1-logloss:0.51841
[19]	validation_0-logloss:0.61159	validation_1-logloss:0.61143[22]	validation_0-logloss:0.57785	validation_1-logloss:0.57679

[14]	validation_0-logloss:0.46803	validation_1-logloss:0.51653
[23]	validation_0-logloss:0.57492	validation_1-logloss:0.57387
[20]	validation_0-logloss:0.61141	validation_1-logloss:0.61139
[15]	validation_0-logloss:0.46531	validation_1-logloss:0.51492
[24]	validation_0-logloss:0.57237	validation_1-logloss:0.57132
[21]	validation_0-logloss:0.60806	validation_1-logloss:0.60809
[25]	validation_0-logloss:0.56970	validation_1-logloss:0.56858
[22]	validation_0-logloss:0.60552	validation_1-logloss:0.60546
[26]	validation_0-logloss:0.56728	validation_1-logloss:0.56620[16]	validation_0-logloss:0.46188	validation_1-logloss:0.51358

[23]	validation_0-logloss:0.60541	validation_1-logloss:0.60544
[27]	validation_0-logloss:0.56520	validation_1-logloss:0.56411
[17]	validation_0-logloss:0.45851	validation_1-logloss:0.51291
[24]	validation_0-logloss:0.60278	validation_1-logloss:0.60280
[28]	validation_0-logloss:0.56323	validation_1-logloss:0.56212
[18]	validation_0-logloss:0.45532	validation_1-logloss:0.51364
[25]	validation_0-logloss:0.60034	validation_1-logloss:0.60035
[29]	validation_0-logloss:0.56135	validation_1-logloss:0.56025
[26]	validation_0-logloss:0.59753	validation_1-logloss:0.59749[30]	validation_0-logloss:0.55914	validation_1-logloss:0.55795

[19]	validation_0-logloss:0.45218	validation_1-logloss:0.51368
[27]	validation_0-logloss:0.59526	validation_1-logloss:0.59520
[31]	validation_0-logloss:0.55718	validation_1-logloss:0.55613
[20]	validation_0-logloss:0.44888	validation_1-logloss:0.51301
[28]	validation_0-logloss:0.59264	validation_1-logloss:0.59249
[32]	validation_0-logloss:0.55510	validation_1-logloss:0.55401
[33]	validation_0-logloss:0.55486	validation_1-logloss:0.55391
[29]	validation_0-logloss:0.59010	validation_1-logloss:0.58984
[21]	validation_0-logloss:0.44391	validation_1-logloss:0.51295
[34]	validation_0-logloss:0.55316	validation_1-logloss:0.55222
[30]	validation_0-logloss:0.58811	validation_1-logloss:0.58779
[35]	validation_0-logloss:0.55154	validation_1-logloss:0.55057
[22]	validation_0-logloss:0.43997	validation_1-logloss:0.51273
[31]	validation_0-logloss:0.58591	validation_1-logloss:0.58559
[36]	validation_0-logloss:0.55001	validation_1-logloss:0.54906
[32]	validation_0-logloss:0.58342	validation_1-logloss:0.58315[37]	validation_0-logloss:0.54820	validation_1-logloss:0.54716

[23]	validation_0-logloss:0.43853	validation_1-logloss:0.51206
[38]	validation_0-logloss:0.54658	validation_1-logloss:0.54562
[33]	validation_0-logloss:0.58116	validation_1-logloss:0.58093
[39]	validation_0-logloss:0.54520	validation_1-logloss:0.54425
[24]	validation_0-logloss:0.43680	validation_1-logloss:0.51297
[34]	validation_0-logloss:0.57876	validation_1-logloss:0.57867
[40]	validation_0-logloss:0.54498	validation_1-logloss:0.54421
[25]	validation_0-logloss:0.43268	validation_1-logloss:0.51341
[35]	validation_0-logloss:0.57697	validation_1-logloss:0.57689
[41]	validation_0-logloss:0.54348	validation_1-logloss:0.54275
[42]	validation_0-logloss:0.54189	validation_1-logloss:0.54112[36]	validation_0-logloss:0.57524	validation_1-logloss:0.57518

[26]	validation_0-logloss:0.43094	validation_1-logloss:0.51371
[43]	validation_0-logloss:0.54039	validation_1-logloss:0.53956
[37]	validation_0-logloss:0.57514	validation_1-logloss:0.57510
[27]	validation_0-logloss:0.42755	validation_1-logloss:0.51316
[44]	validation_0-logloss:0.53894	validation_1-logloss:0.53820
[38]	validation_0-logloss:0.57327	validation_1-logloss:0.57322
[45]	validation_0-logloss:0.53780	validation_1-logloss:0.53709
[39]	validation_0-logloss:0.57163	validation_1-logloss:0.57152
[28]	validation_0-logloss:0.42610	validation_1-logloss:0.51290
[46]	validation_0-logloss:0.53647	validation_1-logloss:0.53578
[40]	validation_0-logloss:0.56954	validation_1-logloss:0.56952
[47]	validation_0-logloss:0.53513	validation_1-logloss:0.53442
[29]	validation_0-logloss:0.42269	validation_1-logloss:0.51455
[41]	validation_0-logloss:0.56793	validation_1-logloss:0.56798
[48]	validation_0-logloss:0.53389	validation_1-logloss:0.53327
[30]	validation_0-logloss:0.42069	validation_1-logloss:0.51545
[49]	validation_0-logloss:0.53274	validation_1-logloss:0.53203[42]	validation_0-logloss:0.56638	validation_1-logloss:0.56645

[43]	validation_0-logloss:0.56498	validation_1-logloss:0.56505
[50]	validation_0-logloss:0.53154	validation_1-logloss:0.53081
[31]	validation_0-logloss:0.41783	validation_1-logloss:0.51645
[44]	validation_0-logloss:0.56305	validation_1-logloss:0.56327
[51]	validation_0-logloss:0.53043	validation_1-logloss:0.52961
[52]	validation_0-logloss:0.52933	validation_1-logloss:0.52858
[32]	validation_0-logloss:0.41613	validation_1-logloss:0.51666[45]	validation_0-logloss:0.56134	validation_1-logloss:0.56152

[53]	validation_0-logloss:0.52847	validation_1-logloss:0.52775
[46]	validation_0-logloss:0.55995	validation_1-logloss:0.56013
[54]	validation_0-logloss:0.52741	validation_1-logloss:0.52661
[33]	validation_0-logloss:0.41443	validation_1-logloss:0.51710
[47]	validation_0-logloss:0.55983	validation_1-logloss:0.56014
[55]	validation_0-logloss:0.52648	validation_1-logloss:0.52574
[34]	validation_0-logloss:0.41305	validation_1-logloss:0.51771
[48]	validation_0-logloss:0.55855	validation_1-logloss:0.55882
[56]	validation_0-logloss:0.52545	validation_1-logloss:0.52463
[57]	validation_0-logloss:0.52439	validation_1-logloss:0.52377
[49]	validation_0-logloss:0.55723	validation_1-logloss:0.55755
[35]	validation_0-logloss:0.41000	validation_1-logloss:0.51907
[58]	validation_0-logloss:0.52357	validation_1-logloss:0.52303
[50]	validation_0-logloss:0.55702	validation_1-logloss:0.55745
[59]	validation_0-logloss:0.52345	validation_1-logloss:0.52300
[36]	validation_0-logloss:0.40846	validation_1-logloss:0.51979
[60]	validation_0-logloss:0.52275	validation_1-logloss:0.52235
[51]	validation_0-logloss:0.55538	validation_1-logloss:0.55579
[61]	validation_0-logloss:0.52186	validation_1-logloss:0.52147
[37]	validation_0-logloss:0.40525	validation_1-logloss:0.52026
[52]	validation_0-logloss:0.55417	validation_1-logloss:0.55457
[62]	validation_0-logloss:0.52100	validation_1-logloss:0.52056
[53]	validation_0-logloss:0.55408	validation_1-logloss:0.55460
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[1725]	validation_0-logloss:0.41624	validation_1-logloss:0.51322
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[1251]	validation_0-logloss:0.03918	validation_1-logloss:0.88815
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[1252]	validation_0-logloss:0.03914	validation_1-logloss:0.88850
[1733]	validation_0-logloss:0.41591	validation_1-logloss:0.51330
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[1734]	validation_0-logloss:0.41588	validation_1-logloss:0.51332
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[1254]	validation_0-logloss:0.03908	validation_1-logloss:0.88875
[1736]	validation_0-logloss:0.41579	validation_1-logloss:0.51333
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[1280]	validation_0-logloss:0.03825	validation_1-logloss:0.89316[2061]	validation_0-logloss:0.39304	validation_1-logloss:0.52795

[1773]	validation_0-logloss:0.41455	validation_1-logloss:0.51354
[2062]	validation_0-logloss:0.39298	validation_1-logloss:0.52800
[1774]	validation_0-logloss:0.41451	validation_1-logloss:0.51358
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[2335]	validation_0-logloss:0.38385	validation_1-logloss:0.53238
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[2344]	validation_0-logloss:0.38356	validation_1-logloss:0.53250
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[2754]	validation_0-logloss:0.38661	validation_1-logloss:0.52446
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[3211]	validation_0-logloss:0.35809	validation_1-logloss:0.54604
[2763]	validation_0-logloss:0.38633	validation_1-logloss:0.52465
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[3213]	validation_0-logloss:0.35804	validation_1-logloss:0.54604[2764]	validation_0-logloss:0.38632	validation_1-logloss:0.52468

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[3214]	validation_0-logloss:0.35801	validation_1-logloss:0.54610
[2765]	validation_0-logloss:0.38630	validation_1-logloss:0.52469
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[2766]	validation_0-logloss:0.38628	validation_1-logloss:0.52467
[3215]	validation_0-logloss:0.35797	validation_1-logloss:0.54610
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[2767]	validation_0-logloss:0.38624	validation_1-logloss:0.52469
[3216]	validation_0-logloss:0.35792	validation_1-logloss:0.54612
[3217]	validation_0-logloss:0.35790	validation_1-logloss:0.54616
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[2768]	validation_0-logloss:0.38622	validation_1-logloss:0.52469
[3218]	validation_0-logloss:0.35787	validation_1-logloss:0.54623
[2003]	validation_0-logloss:0.02529	validation_1-logloss:0.98587
[3219]	validation_0-logloss:0.35785	validation_1-logloss:0.54622
[2769]	validation_0-logloss:0.38619	validation_1-logloss:0.52471
[2770]	validation_0-logloss:0.38615	validation_1-logloss:0.52468
[3220]	validation_0-logloss:0.35781	validation_1-logloss:0.54620
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[2771]	validation_0-logloss:0.38613	validation_1-logloss:0.52470
[3221]	validation_0-logloss:0.35779	validation_1-logloss:0.54620
[2005]	validation_0-logloss:0.02527	validation_1-logloss:0.98602[2772]	validation_0-logloss:0.38610	validation_1-logloss:0.52470

[3222]	validation_0-logloss:0.35776	validation_1-logloss:0.54626
[2773]	validation_0-logloss:0.38609	validation_1-logloss:0.52476
[3223]	validation_0-logloss:0.35774	validation_1-logloss:0.54631
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[2774]	validation_0-logloss:0.38608	validation_1-logloss:0.52474
[3224]	validation_0-logloss:0.35772	validation_1-logloss:0.54635
[2775]	validation_0-logloss:0.38607	validation_1-logloss:0.52475
[2007]	validation_0-logloss:0.02526	validation_1-logloss:0.98610
[3225]	validation_0-logloss:0.35768	validation_1-logloss:0.54635
[2008]	validation_0-logloss:0.02524	validation_1-logloss:0.98641
[3226]	validation_0-logloss:0.35766	validation_1-logloss:0.54628
[2776]	validation_0-logloss:0.38602	validation_1-logloss:0.52478
[2009]	validation_0-logloss:0.02523	validation_1-logloss:0.98637
[3227]	validation_0-logloss:0.35763	validation_1-logloss:0.54633
[2777]	validation_0-logloss:0.38599	validation_1-logloss:0.52480
[3228]	validation_0-logloss:0.35759	validation_1-logloss:0.54636
[2778]	validation_0-logloss:0.38598	validation_1-logloss:0.52482[2010]	validation_0-logloss:0.02521	validation_1-logloss:0.98653

[3229]	validation_0-logloss:0.35756	validation_1-logloss:0.54640
[2779]	validation_0-logloss:0.38597	validation_1-logloss:0.52483
[2011]	validation_0-logloss:0.02520	validation_1-logloss:0.98667
[3230]	validation_0-logloss:0.35755	validation_1-logloss:0.54647
[3231]	validation_0-logloss:0.35752	validation_1-logloss:0.54649
[2780]	validation_0-logloss:0.38595	validation_1-logloss:0.52486
[2012]	validation_0-logloss:0.02519	validation_1-logloss:0.98681[3232]	validation_0-logloss:0.35747	validation_1-logloss:0.54646

[3233]	validation_0-logloss:0.35745	validation_1-logloss:0.54650
[2781]	validation_0-logloss:0.38592	validation_1-logloss:0.52486
[2013]	validation_0-logloss:0.02518	validation_1-logloss:0.98682
[3234]	validation_0-logloss:0.35741	validation_1-logloss:0.54652
[2782]	validation_0-logloss:0.38589	validation_1-logloss:0.52488
[2783]	validation_0-logloss:0.38587	validation_1-logloss:0.52487
[3235]	validation_0-logloss:0.35737	validation_1-logloss:0.54653
[2014]	validation_0-logloss:0.02517	validation_1-logloss:0.98692
[3236]	validation_0-logloss:0.35734	validation_1-logloss:0.54658
[2015]	validation_0-logloss:0.02516	validation_1-logloss:0.98696
[2784]	validation_0-logloss:0.38586	validation_1-logloss:0.52488[3237]	validation_0-logloss:0.35731	validation_1-logloss:0.54661

[2785]	validation_0-logloss:0.38582	validation_1-logloss:0.52489
[3238]	validation_0-logloss:0.35730	validation_1-logloss:0.54670
[2016]	validation_0-logloss:0.02515	validation_1-logloss:0.98721
[2786]	validation_0-logloss:0.38581	validation_1-logloss:0.52490
[3239]	validation_0-logloss:0.35728	validation_1-logloss:0.54671
[2787]	validation_0-logloss:0.38579	validation_1-logloss:0.52488[3240]	validation_0-logloss:0.35724	validation_1-logloss:0.54672

[2017]	validation_0-logloss:0.02513	validation_1-logloss:0.98739
[3241]	validation_0-logloss:0.35722	validation_1-logloss:0.54670
[2788]	validation_0-logloss:0.38577	validation_1-logloss:0.52490
[3242]	validation_0-logloss:0.35718	validation_1-logloss:0.54670
[2018]	validation_0-logloss:0.02512	validation_1-logloss:0.98749
[2789]	validation_0-logloss:0.38575	validation_1-logloss:0.52488
[3243]	validation_0-logloss:0.35715	validation_1-logloss:0.54671
[3244]	validation_0-logloss:0.35711	validation_1-logloss:0.54679[2790]	validation_0-logloss:0.38573	validation_1-logloss:0.52490

[2019]	validation_0-logloss:0.02511	validation_1-logloss:0.98751
[2791]	validation_0-logloss:0.38571	validation_1-logloss:0.52489
[3245]	validation_0-logloss:0.35708	validation_1-logloss:0.54683
[2020]	validation_0-logloss:0.02511	validation_1-logloss:0.98746
[2792]	validation_0-logloss:0.38566	validation_1-logloss:0.52489
[3246]	validation_0-logloss:0.35707	validation_1-logloss:0.54686
[3247]	validation_0-logloss:0.35703	validation_1-logloss:0.54690
[2793]	validation_0-logloss:0.38562	validation_1-logloss:0.52488[2021]	validation_0-logloss:0.02510	validation_1-logloss:0.98775

[3248]	validation_0-logloss:0.35700	validation_1-logloss:0.54688
[3249]	validation_0-logloss:0.35698	validation_1-logloss:0.54683
[2794]	validation_0-logloss:0.38560	validation_1-logloss:0.52488
[2022]	validation_0-logloss:0.02508	validation_1-logloss:0.98793
[3250]	validation_0-logloss:0.35696	validation_1-logloss:0.54684
[2795]	validation_0-logloss:0.38556	validation_1-logloss:0.52490
[2023]	validation_0-logloss:0.02508	validation_1-logloss:0.98798
[3251]	validation_0-logloss:0.35692	validation_1-logloss:0.54685
[2796]	validation_0-logloss:0.38554	validation_1-logloss:0.52494
[3252]	validation_0-logloss:0.35689	validation_1-logloss:0.54686
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[3255]	validation_0-logloss:0.35676	validation_1-logloss:0.54695
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[2026]	validation_0-logloss:0.02506	validation_1-logloss:0.98821
[2800]	validation_0-logloss:0.38540	validation_1-logloss:0.52508
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[3257]	validation_0-logloss:0.35671	validation_1-logloss:0.54695
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[3258]	validation_0-logloss:0.35669	validation_1-logloss:0.54696
[2803]	validation_0-logloss:0.38530	validation_1-logloss:0.52510[2027]	validation_0-logloss:0.02506	validation_1-logloss:0.98823

[3259]	validation_0-logloss:0.35666	validation_1-logloss:0.54699
[2804]	validation_0-logloss:0.38529	validation_1-logloss:0.52514
[3260]	validation_0-logloss:0.35665	validation_1-logloss:0.54705
[2028]	validation_0-logloss:0.02505	validation_1-logloss:0.98843
[2805]	validation_0-logloss:0.38526	validation_1-logloss:0.52513
[3261]	validation_0-logloss:0.35662	validation_1-logloss:0.54702
[2029]	validation_0-logloss:0.02504	validation_1-logloss:0.98863
[3262]	validation_0-logloss:0.35658	validation_1-logloss:0.54703
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[4475]	validation_0-logloss:0.01474	validation_1-logloss:1.11896
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[1034]	validation_0-logloss:0.43652	validation_1-logloss:0.52013[4476]	validation_0-logloss:0.01474	validation_1-logloss:1.11898

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[1035]	validation_0-logloss:0.43647	validation_1-logloss:0.52021
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[1036]	validation_0-logloss:0.43642	validation_1-logloss:0.52017
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[1039]	validation_0-logloss:0.43625	validation_1-logloss:0.52028
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[1040]	validation_0-logloss:0.43620	validation_1-logloss:0.52025
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[1041]	validation_0-logloss:0.43615	validation_1-logloss:0.52028
[4480]	validation_0-logloss:0.01474	validation_1-logloss:1.11910
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[4481]	validation_0-logloss:0.01474	validation_1-logloss:1.11912
[1042]	validation_0-logloss:0.43614	validation_1-logloss:0.52027
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[1044]	validation_0-logloss:0.43600	validation_1-logloss:0.52027
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[1045]	validation_0-logloss:0.43596	validation_1-logloss:0.52027
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[2004]	validation_0-logloss:0.38741	validation_1-logloss:0.53856
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[1047]	validation_0-logloss:0.43587	validation_1-logloss:0.52030
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[1049]	validation_0-logloss:0.43576	validation_1-logloss:0.52028
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[1050]	validation_0-logloss:0.43574	validation_1-logloss:0.52032
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[4487]	validation_0-logloss:0.01473	validation_1-logloss:1.11919
[2010]	validation_0-logloss:0.38720	validation_1-logloss:0.53860
[1051]	validation_0-logloss:0.43569	validation_1-logloss:0.52034
[2011]	validation_0-logloss:0.38715	validation_1-logloss:0.53859
[1052]	validation_0-logloss:0.43562	validation_1-logloss:0.52034
[4488]	validation_0-logloss:0.01473	validation_1-logloss:1.11924
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[1053]	validation_0-logloss:0.43559	validation_1-logloss:0.52037
[2013]	validation_0-logloss:0.38708	validation_1-logloss:0.53863
[4489]	validation_0-logloss:0.01473	validation_1-logloss:1.11926
[2014]	validation_0-logloss:0.38704	validation_1-logloss:0.53863
[1054]	validation_0-logloss:0.43554	validation_1-logloss:0.52036
[2015]	validation_0-logloss:0.38702	validation_1-logloss:0.53860
[1055]	validation_0-logloss:0.43549	validation_1-logloss:0.52037
[4490]	validation_0-logloss:0.01473	validation_1-logloss:1.11939
[2016]	validation_0-logloss:0.38698	validation_1-logloss:0.53859
[1056]	validation_0-logloss:0.43543	validation_1-logloss:0.52040
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[4491]	validation_0-logloss:0.01473	validation_1-logloss:1.11941
[1057]	validation_0-logloss:0.43536	validation_1-logloss:0.52041[2018]	validation_0-logloss:0.38690	validation_1-logloss:0.53865

[4492]	validation_0-logloss:0.01472	validation_1-logloss:1.11937
[2019]	validation_0-logloss:0.38686	validation_1-logloss:0.53868
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[2047]	validation_0-logloss:0.38593	validation_1-logloss:0.53876
[1081]	validation_0-logloss:0.43438	validation_1-logloss:0.52073[4511]	validation_0-logloss:0.01469	validation_1-logloss:1.11963

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[1082]	validation_0-logloss:0.43434	validation_1-logloss:0.52078
[2049]	validation_0-logloss:0.38584	validation_1-logloss:0.53873
[1083]	validation_0-logloss:0.43431	validation_1-logloss:0.52084
[2050]	validation_0-logloss:0.38581	validation_1-logloss:0.53869[4512]	validation_0-logloss:0.01469	validation_1-logloss:1.11966

[1084]	validation_0-logloss:0.43427	validation_1-logloss:0.52089
[2051]	validation_0-logloss:0.38576	validation_1-logloss:0.53873
[4513]	validation_0-logloss:0.01469	validation_1-logloss:1.11967
[1085]	validation_0-logloss:0.43422	validation_1-logloss:0.52091
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[4479]	validation_0-logloss:0.34459	validation_1-logloss:0.55812
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[981]	validation_0-logloss:0.43550	validation_1-logloss:0.51334
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[4485]	validation_0-logloss:0.34449	validation_1-logloss:0.55821
[982]	validation_0-logloss:0.43545	validation_1-logloss:0.51340
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[4486]	validation_0-logloss:0.34447	validation_1-logloss:0.55820
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[4487]	validation_0-logloss:0.34447	validation_1-logloss:0.55821
[985]	validation_0-logloss:0.43535	validation_1-logloss:0.51345
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[986]	validation_0-logloss:0.43530	validation_1-logloss:0.51349
[987]	validation_0-logloss:0.43526	validation_1-logloss:0.51352
[4489]	validation_0-logloss:0.34445	validation_1-logloss:0.55820
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[3923]	validation_0-logloss:0.33689	validation_1-logloss:0.55632
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[3920]	validation_0-logloss:0.01541	validation_1-logloss:1.09839
[3926]	validation_0-logloss:0.33684	validation_1-logloss:0.55636
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[3921]	validation_0-logloss:0.01541	validation_1-logloss:1.09843[3927]	validation_0-logloss:0.33680	validation_1-logloss:0.55637

[1986]	validation_0-logloss:0.40311	validation_1-logloss:0.52486
[3928]	validation_0-logloss:0.33678	validation_1-logloss:0.55639
[3922]	validation_0-logloss:0.01540	validation_1-logloss:1.09840
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[3929]	validation_0-logloss:0.33676	validation_1-logloss:0.55640
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[3930]	validation_0-logloss:0.33675	validation_1-logloss:0.55641
[3923]	validation_0-logloss:0.01540	validation_1-logloss:1.09847
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[3932]	validation_0-logloss:0.33671	validation_1-logloss:0.55650
[3924]	validation_0-logloss:0.01540	validation_1-logloss:1.09847
[1990]	validation_0-logloss:0.40304	validation_1-logloss:0.52485
[3933]	validation_0-logloss:0.33670	validation_1-logloss:0.55650
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[3925]	validation_0-logloss:0.01540	validation_1-logloss:1.09852[3934]	validation_0-logloss:0.33668	validation_1-logloss:0.55648

[3935]	validation_0-logloss:0.33667	validation_1-logloss:0.55652
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[3936]	validation_0-logloss:0.33664	validation_1-logloss:0.55653
[3926]	validation_0-logloss:0.01539	validation_1-logloss:1.09859
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[3937]	validation_0-logloss:0.33663	validation_1-logloss:0.55646
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[3938]	validation_0-logloss:0.33661	validation_1-logloss:0.55642
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[3939]	validation_0-logloss:0.33659	validation_1-logloss:0.55643
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[3940]	validation_0-logloss:0.33656	validation_1-logloss:0.55648
[3929]	validation_0-logloss:0.01539	validation_1-logloss:1.09858
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[3941]	validation_0-logloss:0.33654	validation_1-logloss:0.55647
[3930]	validation_0-logloss:0.01539	validation_1-logloss:1.09860
[1998]	validation_0-logloss:0.40276	validation_1-logloss:0.52493
[3931]	validation_0-logloss:0.01539	validation_1-logloss:1.09860[3942]	validation_0-logloss:0.33653	validation_1-logloss:0.55651

[1999]	validation_0-logloss:0.40274	validation_1-logloss:0.52496
[3943]	validation_0-logloss:0.33651	validation_1-logloss:0.55654
[3932]	validation_0-logloss:0.01539	validation_1-logloss:1.09864
[2000]	validation_0-logloss:0.40271	validation_1-logloss:0.52497
[3944]	validation_0-logloss:0.33648	validation_1-logloss:0.55665
[2001]	validation_0-logloss:0.40268	validation_1-logloss:0.52500
[3933]	validation_0-logloss:0.01538	validation_1-logloss:1.09861
[3945]	validation_0-logloss:0.33644	validation_1-logloss:0.55668
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[3946]	validation_0-logloss:0.33642	validation_1-logloss:0.55668
[3934]	validation_0-logloss:0.01538	validation_1-logloss:1.09857
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[3947]	validation_0-logloss:0.33639	validation_1-logloss:0.55666
[2004]	validation_0-logloss:0.40257	validation_1-logloss:0.52503
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[3935]	validation_0-logloss:0.01538	validation_1-logloss:1.09866
[3949]	validation_0-logloss:0.33635	validation_1-logloss:0.55664
[3936]	validation_0-logloss:0.01538	validation_1-logloss:1.09862
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[3950]	validation_0-logloss:0.33632	validation_1-logloss:0.55661
[3937]	validation_0-logloss:0.01538	validation_1-logloss:1.09862
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[3951]	validation_0-logloss:0.33631	validation_1-logloss:0.55659
[3952]	validation_0-logloss:0.33629	validation_1-logloss:0.55658[3938]	validation_0-logloss:0.01537	validation_1-logloss:1.09867

[2007]	validation_0-logloss:0.40245	validation_1-logloss:0.52506
[3953]	validation_0-logloss:0.33628	validation_1-logloss:0.55663
[2008]	validation_0-logloss:0.40242	validation_1-logloss:0.52508
[3939]	validation_0-logloss:0.01537	validation_1-logloss:1.09863
[3954]	validation_0-logloss:0.33624	validation_1-logloss:0.55675
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[3940]	validation_0-logloss:0.01537	validation_1-logloss:1.09870
[3955]	validation_0-logloss:0.33622	validation_1-logloss:0.55673
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[3956]	validation_0-logloss:0.33621	validation_1-logloss:0.55678
[3941]	validation_0-logloss:0.01537	validation_1-logloss:1.09882[2011]	validation_0-logloss:0.40230	validation_1-logloss:0.52508

[3957]	validation_0-logloss:0.33618	validation_1-logloss:0.55673
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[3958]	validation_0-logloss:0.33615	validation_1-logloss:0.55673
[3942]	validation_0-logloss:0.01536	validation_1-logloss:1.09888
[3959]	validation_0-logloss:0.33613	validation_1-logloss:0.55671
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[3943]	validation_0-logloss:0.01536	validation_1-logloss:1.09894
[3960]	validation_0-logloss:0.33611	validation_1-logloss:0.55676
[2014]	validation_0-logloss:0.40223	validation_1-logloss:0.52511
[3961]	validation_0-logloss:0.33609	validation_1-logloss:0.55684
[3944]	validation_0-logloss:0.01536	validation_1-logloss:1.09897
[3962]	validation_0-logloss:0.33606	validation_1-logloss:0.55688
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[3945]	validation_0-logloss:0.01535	validation_1-logloss:1.09904
[3963]	validation_0-logloss:0.33603	validation_1-logloss:0.55691
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[3964]	validation_0-logloss:0.33601	validation_1-logloss:0.55688
[3946]	validation_0-logloss:0.01535	validation_1-logloss:1.09906
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[3965]	validation_0-logloss:0.33598	validation_1-logloss:0.55693
[3947]	validation_0-logloss:0.01535	validation_1-logloss:1.09910
[2018]	validation_0-logloss:0.40214	validation_1-logloss:0.52513
[3966]	validation_0-logloss:0.33595	validation_1-logloss:0.55696
[3967]	validation_0-logloss:0.33591	validation_1-logloss:0.55704
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[3948]	validation_0-logloss:0.01535	validation_1-logloss:1.09910
[3968]	validation_0-logloss:0.33588	validation_1-logloss:0.55702
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[3969]	validation_0-logloss:0.33585	validation_1-logloss:0.55702[2021]	validation_0-logloss:0.40205	validation_1-logloss:0.52515

[3949]	validation_0-logloss:0.01535	validation_1-logloss:1.09913
[2022]	validation_0-logloss:0.40201	validation_1-logloss:0.52515
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[3952]	validation_0-logloss:0.01534	validation_1-logloss:1.09919
[3975]	validation_0-logloss:0.33574	validation_1-logloss:0.55717
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[2032]	validation_0-logloss:0.40169	validation_1-logloss:0.52520

[3982]	validation_0-logloss:0.33563	validation_1-logloss:0.55723
[2033]	validation_0-logloss:0.40163	validation_1-logloss:0.52523
[3957]	validation_0-logloss:0.01533	validation_1-logloss:1.09920
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[2038]	validation_0-logloss:0.40150	validation_1-logloss:0.52529
[3989]	validation_0-logloss:0.33547	validation_1-logloss:0.55731[3961]	validation_0-logloss:0.01532	validation_1-logloss:1.09935

[2039]	validation_0-logloss:0.40149	validation_1-logloss:0.52529
[3990]	validation_0-logloss:0.33545	validation_1-logloss:0.55729
[3962]	validation_0-logloss:0.01532	validation_1-logloss:1.09940
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[2040]	validation_0-logloss:0.40147	validation_1-logloss:0.52532
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[3967]	validation_0-logloss:0.01531	validation_1-logloss:1.09960
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[2047]	validation_0-logloss:0.40123	validation_1-logloss:0.52540
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[2051]	validation_0-logloss:0.40113	validation_1-logloss:0.52545
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[2052]	validation_0-logloss:0.40111	validation_1-logloss:0.52544
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[3972]	validation_0-logloss:0.01530	validation_1-logloss:1.09995
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[3979]	validation_0-logloss:0.01528	validation_1-logloss:1.10002
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[2064]	validation_0-logloss:0.40070	validation_1-logloss:0.52559
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[2066]	validation_0-logloss:0.40068	validation_1-logloss:0.52560
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[3983]	validation_0-logloss:0.01527	validation_1-logloss:1.10013
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[4026]	validation_0-logloss:0.33470	validation_1-logloss:0.55776
[2068]	validation_0-logloss:0.40065	validation_1-logloss:0.52560[3984]	validation_0-logloss:0.01527	validation_1-logloss:1.10016

[4027]	validation_0-logloss:0.33467	validation_1-logloss:0.55780
[2069]	validation_0-logloss:0.40063	validation_1-logloss:0.52561
[3985]	validation_0-logloss:0.01527	validation_1-logloss:1.10029
[4028]	validation_0-logloss:0.33466	validation_1-logloss:0.55784
[3986]	validation_0-logloss:0.01527	validation_1-logloss:1.10030[2070]	validation_0-logloss:0.40062	validation_1-logloss:0.52563

[3987]	validation_0-logloss:0.01527	validation_1-logloss:1.10030
[4029]	validation_0-logloss:0.33462	validation_1-logloss:0.55777
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[4660]	validation_0-logloss:0.01414	validation_1-logloss:1.11962
[2884]	validation_0-logloss:0.37889	validation_1-logloss:0.53452
[4984]	validation_0-logloss:0.31384	validation_1-logloss:0.57173
[2885]	validation_0-logloss:0.37885	validation_1-logloss:0.53452
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[4661]	validation_0-logloss:0.01414	validation_1-logloss:1.11972
[4986]	validation_0-logloss:0.31380	validation_1-logloss:0.57180[2886]	validation_0-logloss:0.37884	validation_1-logloss:0.53453

[4662]	validation_0-logloss:0.01413	validation_1-logloss:1.11973
[4987]	validation_0-logloss:0.31378	validation_1-logloss:0.57179
[2887]	validation_0-logloss:0.37883	validation_1-logloss:0.53454
[4988]	validation_0-logloss:0.31376	validation_1-logloss:0.57185
[4663]	validation_0-logloss:0.01413	validation_1-logloss:1.11979
[2888]	validation_0-logloss:0.37881	validation_1-logloss:0.53452
[4989]	validation_0-logloss:0.31374	validation_1-logloss:0.57182
[2889]	validation_0-logloss:0.37879	validation_1-logloss:0.53453
[4990]	validation_0-logloss:0.31372	validation_1-logloss:0.57184
[4664]	validation_0-logloss:0.01413	validation_1-logloss:1.11991
[4991]	validation_0-logloss:0.31371	validation_1-logloss:0.57186
[2890]	validation_0-logloss:0.37876	validation_1-logloss:0.53454
[4665]	validation_0-logloss:0.01413	validation_1-logloss:1.12000
[4992]	validation_0-logloss:0.31370	validation_1-logloss:0.57183
[2891]	validation_0-logloss:0.37875	validation_1-logloss:0.53454
[4993]	validation_0-logloss:0.31368	validation_1-logloss:0.57186
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[2892]	validation_0-logloss:0.37872	validation_1-logloss:0.53451
[2893]	validation_0-logloss:0.37870	validation_1-logloss:0.53455
[4994]	validation_0-logloss:0.31367	validation_1-logloss:0.57186
[4667]	validation_0-logloss:0.01412	validation_1-logloss:1.12003
[2894]	validation_0-logloss:0.37868	validation_1-logloss:0.53452
[4995]	validation_0-logloss:0.31366	validation_1-logloss:0.57186
[2895]	validation_0-logloss:0.37865	validation_1-logloss:0.53449
[4996]	validation_0-logloss:0.31363	validation_1-logloss:0.57187
[4668]	validation_0-logloss:0.01412	validation_1-logloss:1.12006
[2896]	validation_0-logloss:0.37862	validation_1-logloss:0.53451
[4997]	validation_0-logloss:0.31360	validation_1-logloss:0.57188
[2897]	validation_0-logloss:0.37859	validation_1-logloss:0.53448
[4998]	validation_0-logloss:0.31359	validation_1-logloss:0.57180
[4669]	validation_0-logloss:0.01412	validation_1-logloss:1.12012
[4999]	validation_0-logloss:0.31356	validation_1-logloss:0.57186
[2898]	validation_0-logloss:0.37859	validation_1-logloss:0.53448
[4670]	validation_0-logloss:0.01412	validation_1-logloss:1.12021
[2899]	validation_0-logloss:0.37857	validation_1-logloss:0.53452
[4671]	validation_0-logloss:0.01412	validation_1-logloss:1.12021
[2900]	validation_0-logloss:0.37853	validation_1-logloss:0.53456
[4672]	validation_0-logloss:0.01412	validation_1-logloss:1.12018
[2901]	validation_0-logloss:0.37852	validation_1-logloss:0.53458
[2902]	validation_0-logloss:0.37848	validation_1-logloss:0.53467
[4673]	validation_0-logloss:0.01412	validation_1-logloss:1.12020
[2903]	validation_0-logloss:0.37847	validation_1-logloss:0.53467
[4674]	validation_0-logloss:0.01411	validation_1-logloss:1.12020
[2904]	validation_0-logloss:0.37846	validation_1-logloss:0.53469
[4675]	validation_0-logloss:0.01411	validation_1-logloss:1.12023
[2905]	validation_0-logloss:0.37842	validation_1-logloss:0.53470
[2906]	validation_0-logloss:0.37841	validation_1-logloss:0.53470
[4676]	validation_0-logloss:0.01411	validation_1-logloss:1.12022
[2907]	validation_0-logloss:0.37840	validation_1-logloss:0.53471
[4677]	validation_0-logloss:0.01411	validation_1-logloss:1.12023
[2908]	validation_0-logloss:0.37838	validation_1-logloss:0.53474
[4678]	validation_0-logloss:0.01411	validation_1-logloss:1.12032
[2909]	validation_0-logloss:0.37836	validation_1-logloss:0.53473
[4679]	validation_0-logloss:0.01411	validation_1-logloss:1.12032
[2910]	validation_0-logloss:0.37833	validation_1-logloss:0.53472
[2911]	validation_0-logloss:0.37830	validation_1-logloss:0.53473
[4680]	validation_0-logloss:0.01410	validation_1-logloss:1.12036
[2912]	validation_0-logloss:0.37828	validation_1-logloss:0.53475
[4681]	validation_0-logloss:0.01410	validation_1-logloss:1.12037
[2913]	validation_0-logloss:0.37826	validation_1-logloss:0.53473
[4682]	validation_0-logloss:0.01410	validation_1-logloss:1.12036
[2914]	validation_0-logloss:0.37825	validation_1-logloss:0.53473
[2915]	validation_0-logloss:0.37820	validation_1-logloss:0.53473
[4683]	validation_0-logloss:0.01410	validation_1-logloss:1.12039
[2916]	validation_0-logloss:0.37818	validation_1-logloss:0.53472

[I 2026-05-05 06:16:05,271] Trial 0 finished with value: 0.7324612164392383 and parameters: {'max_depth': 3, 'min_child_weight': 1, 'reg_alpha': 0.003840239840825237, 'reg_lambda': 2.290859732636261e-06, 'gamma': 0.05256769110802507, 'subsample': 0.7981363566199738, 'colsample_bytree': 0.8207757910147984, 'colsample_bylevel': 0.9895671973967499, 'learning_rate': 0.029980378757743585, 'max_bin': 128}. Best is trial 0 with value: 0.7324612164392383.

[2917]	validation_0-logloss:0.37815	validation_1-logloss:0.53473
[4684]	validation_0-logloss:0.01410	validation_1-logloss:1.12041
[4685]	validation_0-logloss:0.01410	validation_1-logloss:1.12041[2918]	validation_0-logloss:0.37813	validation_1-logloss:0.53475

[2919]	validation_0-logloss:0.37810	validation_1-logloss:0.53473
[4686]	validation_0-logloss:0.01410	validation_1-logloss:1.12041
[2920]	validation_0-logloss:0.37809	validation_1-logloss:0.53472
[4687]	validation_0-logloss:0.01410	validation_1-logloss:1.12043
[2921]	validation_0-logloss:0.37807	validation_1-logloss:0.53471
[4688]	validation_0-logloss:0.01410	validation_1-logloss:1.12043
[2922]	validation_0-logloss:0.37803	validation_1-logloss:0.53477
[4689]	validation_0-logloss:0.01410	validation_1-logloss:1.12043
[2923]	validation_0-logloss:0.37802	validation_1-logloss:0.53477
[2924]	validation_0-logloss:0.37798	validation_1-logloss:0.53480
[4690]	validation_0-logloss:0.01410	validation_1-logloss:1.12043
[2925]	validation_0-logloss:0.37795	validation_1-logloss:0.53480
[4691]	validation_0-logloss:0.01410	validation_1-logloss:1.12043
[2926]	validation_0-logloss:0.37792	validation_1-logloss:0.53480
[2927]	validation_0-logloss:0.37790	validation_1-logloss:0.53482
[4692]	validation_0-logloss:0.01409	validation_1-logloss:1.12043
[2928]	validation_0-logloss:0.37787	validation_1-logloss:0.53488
[2929]	validation_0-logloss:0.37784	validation_1-logloss:0.53486[4693]	validation_0-logloss:0.01409	validation_1-logloss:1.12044

[4694]	validation_0-logloss:0.01409	validation_1-logloss:1.12044
[2930]	validation_0-logloss:0.37782	validation_1-logloss:0.53485
[4695]	validation_0-logloss:0.01409	validation_1-logloss:1.12053
[2931]	validation_0-logloss:0.37780	validation_1-logloss:0.53482
[4696]	validation_0-logloss:0.01409	validation_1-logloss:1.12053
[2932]	validation_0-logloss:0.37777	validation_1-logloss:0.53482
[4697]	validation_0-logloss:0.01409	validation_1-logloss:1.12054
[2933]	validation_0-logloss:0.37773	validation_1-logloss:0.53485
[4698]	validation_0-logloss:0.01409	validation_1-logloss:1.12058
[2934]	validation_0-logloss:0.37770	validation_1-logloss:0.53490
[2935]	validation_0-logloss:0.37769	validation_1-logloss:0.53487
[4699]	validation_0-logloss:0.01409	validation_1-logloss:1.12070
[4700]	validation_0-logloss:0.01409	validation_1-logloss:1.12070
[2936]	validation_0-logloss:0.37766	validation_1-logloss:0.53489
[2937]	validation_0-logloss:0.37761	validation_1-logloss:0.53493[4701]	validation_0-logloss:0.01408	validation_1-logloss:1.12074

[2938]	validation_0-logloss:0.37757	validation_1-logloss:0.53490
[4702]	validation_0-logloss:0.01408	validation_1-logloss:1.12073
[2939]	validation_0-logloss:0.37755	validation_1-logloss:0.53489
[4703]	validation_0-logloss:0.01408	validation_1-logloss:1.12068
[2940]	validation_0-logloss:0.37752	validation_1-logloss:0.53490
[2941]	validation_0-logloss:0.37750	validation_1-logloss:0.53489
[4704]	validation_0-logloss:0.01408	validation_1-logloss:1.12066
[2942]	validation_0-logloss:0.37750	validation_1-logloss:0.53493
[4705]	validation_0-logloss:0.01408	validation_1-logloss:1.12066
[2943]	validation_0-logloss:0.37748	validation_1-logloss:0.53493
[4706]	validation_0-logloss:0.01408	validation_1-logloss:1.12070
[4707]	validation_0-logloss:0.01408	validation_1-logloss:1.12070
[2944]	validation_0-logloss:0.37747	validation_1-logloss:0.53495
[4708]	validation_0-logloss:0.01408	validation_1-logloss:1.12074
[2945]	validation_0-logloss:0.37745	validation_1-logloss:0.53494
[4709]	validation_0-logloss:0.01408	validation_1-logloss:1.12072
[2946]	validation_0-logloss:0.37741	validation_1-logloss:0.53497
[4710]	validation_0-logloss:0.01408	validation_1-logloss:1.12070
[2947]	validation_0-logloss:0.37740	validation_1-logloss:0.53499
[2948]	validation_0-logloss:0.37738	validation_1-logloss:0.53499
[4711]	validation_0-logloss:0.01408	validation_1-logloss:1.12070
[2949]	validation_0-logloss:0.37735	validation_1-logloss:0.53500
[4712]	validation_0-logloss:0.01407	validation_1-logloss:1.12072
[2950]	validation_0-logloss:0.37732	validation_1-logloss:0.53499
[4713]	validation_0-logloss:0.01407	validation_1-logloss:1.12079
[2951]	validation_0-logloss:0.37731	validation_1-logloss:0.53501
[4714]	validation_0-logloss:0.01407	validation_1-logloss:1.12083
[2952]	validation_0-logloss:0.37731	validation_1-logloss:0.53502
[2953]	validation_0-logloss:0.37729	validation_1-logloss:0.53505
[4715]	validation_0-logloss:0.01407	validation_1-logloss:1.12082
[2954]	validation_0-logloss:0.37726	validation_1-logloss:0.53507
[4716]	validation_0-logloss:0.01407	validation_1-logloss:1.12092
[2955]	validation_0-logloss:0.37725	validation_1-logloss:0.53509
[4717]	validation_0-logloss:0.01407	validation_1-logloss:1.12094
[2956]	validation_0-logloss:0.37723	validation_1-logloss:0.53510
[2957]	validation_0-logloss:0.37721	validation_1-logloss:0.53509
[4718]	validation_0-logloss:0.01407	validation_1-logloss:1.12097
[2958]	validation_0-logloss:0.37719	validation_1-logloss:0.53514
[2959]	validation_0-logloss:0.37719	validation_1-logloss:0.53517
[4719]	validation_0-logloss:0.01407	validation_1-logloss:1.12101
[2960]	validation_0-logloss:0.37718	validation_1-logloss:0.53514
[4720]	validation_0-logloss:0.01407	validation_1-logloss:1.12107
[2961]	validation_0-logloss:0.37714	validation_1-logloss:0.53519
[2962]	validation_0-logloss:0.37711	validation_1-logloss:0.53522
[4721]	validation_0-logloss:0.01406	validation_1-logloss:1.12110
[2963]	validation_0-logloss:0.37709	validation_1-logloss:0.53527
[4722]	validation_0-logloss:0.01406	validation_1-logloss:1.12112
[2964]	validation_0-logloss:0.37706	validation_1-logloss:0.53530
[2965]	validation_0-logloss:0.37703	validation_1-logloss:0.53529
[4723]	validation_0-logloss:0.01406	validation_1-logloss:1.12111
[2966]	validation_0-logloss:0.37697	validation_1-logloss:0.53529
[4724]	validation_0-logloss:0.01406	validation_1-logloss:1.12112
[2967]	validation_0-logloss:0.37696	validation_1-logloss:0.53529
[4725]	validation_0-logloss:0.01406	validation_1-logloss:1.12117
[2968]	validation_0-logloss:0.37695	validation_1-logloss:0.53527
[2969]	validation_0-logloss:0.37691	validation_1-logloss:0.53531
[4726]	validation_0-logloss:0.01406	validation_1-logloss:1.12122
[2970]	validation_0-logloss:0.37688	validation_1-logloss:0.53534
[4727]	validation_0-logloss:0.01406	validation_1-logloss:1.12121
[2971]	validation_0-logloss:0.37687	validation_1-logloss:0.53534
[4728]	validation_0-logloss:0.01406	validation_1-logloss:1.12125
[4729]	validation_0-logloss:0.01406	validation_1-logloss:1.12123
[2972]	validation_0-logloss:0.37684	validation_1-logloss:0.53529
[2973]	validation_0-logloss:0.37679	validation_1-logloss:0.53526
[4730]	validation_0-logloss:0.01406	validation_1-logloss:1.12122
[4731]	validation_0-logloss:0.01406	validation_1-logloss:1.12122
[2974]	validation_0-logloss:0.37676	validation_1-logloss:0.53528
[4732]	validation_0-logloss:0.01405	validation_1-logloss:1.12121[2975]	validation_0-logloss:0.37673	validation_1-logloss:0.53532

[2976]	validation_0-logloss:0.37672	validation_1-logloss:0.53534
[4733]	validation_0-logloss:0.01405	validation_1-logloss:1.12121
[2977]	validation_0-logloss:0.37669	validation_1-logloss:0.53535
[4734]	validation_0-logloss:0.01405	validation_1-logloss:1.12121
[2978]	validation_0-logloss:0.37668	validation_1-logloss:0.53539
[4735]	validation_0-logloss:0.01405	validation_1-logloss:1.12123
[2979]	validation_0-logloss:0.37666	validation_1-logloss:0.53540
[4736]	validation_0-logloss:0.01405	validation_1-logloss:1.12116
[2980]	validation_0-logloss:0.37665	validation_1-logloss:0.53539
[4737]	validation_0-logloss:0.01405	validation_1-logloss:1.12121
[2981]	validation_0-logloss:0.37665	validation_1-logloss:0.53536
[2982]	validation_0-logloss:0.37662	validation_1-logloss:0.53539
[4738]	validation_0-logloss:0.01405	validation_1-logloss:1.12125
[4739]	validation_0-logloss:0.01405	validation_1-logloss:1.12123
[2983]	validation_0-logloss:0.37659	validation_1-logloss:0.53539
[2984]	validation_0-logloss:0.37659	validation_1-logloss:0.53537
[4740]	validation_0-logloss:0.01405	validation_1-logloss:1.12125
[2985]	validation_0-logloss:0.37658	validation_1-logloss:0.53539
[2986]	validation_0-logloss:0.37655	validation_1-logloss:0.53542
[4741]	validation_0-logloss:0.01405	validation_1-logloss:1.12124
[2987]	validation_0-logloss:0.37652	validation_1-logloss:0.53542
[4742]	validation_0-logloss:0.01405	validation_1-logloss:1.12125
[2988]	validation_0-logloss:0.37649	validation_1-logloss:0.53541
[4743]	validation_0-logloss:0.01404	validation_1-logloss:1.12133
[2989]	validation_0-logloss:0.37647	validation_1-logloss:0.53541
[4744]	validation_0-logloss:0.01404	validation_1-logloss:1.12128
[2990]	validation_0-logloss:0.37645	validation_1-logloss:0.53540
[4745]	validation_0-logloss:0.01404	validation_1-logloss:1.12126
[2991]	validation_0-logloss:0.37641	validation_1-logloss:0.53537
[2992]	validation_0-logloss:0.37638	validation_1-logloss:0.53541
[4746]	validation_0-logloss:0.01404	validation_1-logloss:1.12128
[2993]	validation_0-logloss:0.37637	validation_1-logloss:0.53541
[4747]	validation_0-logloss:0.01404	validation_1-logloss:1.12128
[2994]	validation_0-logloss:0.37636	validation_1-logloss:0.53541
[2995]	validation_0-logloss:0.37634	validation_1-logloss:0.53544
[4748]	validation_0-logloss:0.01404	validation_1-logloss:1.12128
[2996]	validation_0-logloss:0.37632	validation_1-logloss:0.53546
[4749]	validation_0-logloss:0.01404	validation_1-logloss:1.12134
[2997]	validation_0-logloss:0.37631	validation_1-logloss:0.53549
[4750]	validation_0-logloss:0.01404	validation_1-logloss:1.12131
[2998]	validation_0-logloss:0.37630	validation_1-logloss:0.53548
[4751]	validation_0-logloss:0.01404	validation_1-logloss:1.12131
[2999]	validation_0-logloss:0.37628	validation_1-logloss:0.53546
[3000]	validation_0-logloss:0.37626	validation_1-logloss:0.53544
[4752]	validation_0-logloss:0.01403	validation_1-logloss:1.12128
[3001]	validation_0-logloss:0.37624	validation_1-logloss:0.53549
[4753]	validation_0-logloss:0.01403	validation_1-logloss:1.12129
[4754]	validation_0-logloss:0.01403	validation_1-logloss:1.12129
[3002]	validation_0-logloss:0.37622	validation_1-logloss:0.53545
[3003]	validation_0-logloss:0.37619	validation_1-logloss:0.53541
[4755]	validation_0-logloss:0.01403	validation_1-logloss:1.12133
[3004]	validation_0-logloss:0.37615	validation_1-logloss:0.53542
[4756]	validation_0-logloss:0.01403	validation_1-logloss:1.12133
[3005]	validation_0-logloss:0.37612	validation_1-logloss:0.53548
[3006]	validation_0-logloss:0.37606	validation_1-logloss:0.53555
[4757]	validation_0-logloss:0.01403	validation_1-logloss:1.12139
[3007]	validation_0-logloss:0.37604	validation_1-logloss:0.53558
[4758]	validation_0-logloss:0.01403	validation_1-logloss:1.12143
[3008]	validation_0-logloss:0.37603	validation_1-logloss:0.53558
[4759]	validation_0-logloss:0.01402	validation_1-logloss:1.12141
[3009]	validation_0-logloss:0.37601	validation_1-logloss:0.53560
[3010]	validation_0-logloss:0.37601	validation_1-logloss:0.53559
[4760]	validation_0-logloss:0.01402	validation_1-logloss:1.12147
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[3011]	validation_0-logloss:0.37599	validation_1-logloss:0.53560
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[3012]	validation_0-logloss:0.37598	validation_1-logloss:0.53560
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[3013]	validation_0-logloss:0.37596	validation_1-logloss:0.53562
[4764]	validation_0-logloss:0.01402	validation_1-logloss:1.12158
[3014]	validation_0-logloss:0.37594	validation_1-logloss:0.53562
[4765]	validation_0-logloss:0.01402	validation_1-logloss:1.12158
[3015]	validation_0-logloss:0.37591	validation_1-logloss:0.53553
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[3016]	validation_0-logloss:0.37588	validation_1-logloss:0.53555
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[3017]	validation_0-logloss:0.37587	validation_1-logloss:0.53555
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[3020]	validation_0-logloss:0.37578	validation_1-logloss:0.53561
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[4904]	validation_0-logloss:0.33821	validation_1-logloss:0.55495
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[4927]	validation_0-logloss:0.33787	validation_1-logloss:0.55519
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[4961]	validation_0-logloss:0.33735	validation_1-logloss:0.55537
[1217]	validation_0-logloss:0.03836	validation_1-logloss:0.90333[4962]	validation_0-logloss:0.33732	validation_1-logloss:0.55539

[1218]	validation_0-logloss:0.03833	validation_1-logloss:0.90367[4963]	validation_0-logloss:0.33729	validation_1-logloss:0.55542

[4964]	validation_0-logloss:0.33727	validation_1-logloss:0.55541
[1219]	validation_0-logloss:0.03831	validation_1-logloss:0.90361
[4965]	validation_0-logloss:0.33724	validation_1-logloss:0.55544
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[1222]	validation_0-logloss:0.03822	validation_1-logloss:0.90414
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[1225]	validation_0-logloss:0.03812	validation_1-logloss:0.90482
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[1235]	validation_0-logloss:0.03783	validation_1-logloss:0.90606
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[1236]	validation_0-logloss:0.03781	validation_1-logloss:0.90611
[4988]	validation_0-logloss:0.33685	validation_1-logloss:0.55547
[1237]	validation_0-logloss:0.03779	validation_1-logloss:0.90613
[4989]	validation_0-logloss:0.33683	validation_1-logloss:0.55548
[4990]	validation_0-logloss:0.33682	validation_1-logloss:0.55549
[1238]	validation_0-logloss:0.03776	validation_1-logloss:0.90644
[4991]	validation_0-logloss:0.33680	validation_1-logloss:0.55547
[1239]	validation_0-logloss:0.03773	validation_1-logloss:0.90692
[4992]	validation_0-logloss:0.33677	validation_1-logloss:0.55545
[1240]	validation_0-logloss:0.03770	validation_1-logloss:0.90726
[4993]	validation_0-logloss:0.33676	validation_1-logloss:0.55545
[1241]	validation_0-logloss:0.03768	validation_1-logloss:0.90731
[4994]	validation_0-logloss:0.33675	validation_1-logloss:0.55548
[4995]	validation_0-logloss:0.33673	validation_1-logloss:0.55549
[1242]	validation_0-logloss:0.03765	validation_1-logloss:0.90753
[4996]	validation_0-logloss:0.33670	validation_1-logloss:0.55551
[1243]	validation_0-logloss:0.03764	validation_1-logloss:0.90762
[4997]	validation_0-logloss:0.33668	validation_1-logloss:0.55549
[4998]	validation_0-logloss:0.33666	validation_1-logloss:0.55548
[1244]	validation_0-logloss:0.03762	validation_1-logloss:0.90763
[1245]	validation_0-logloss:0.03758	validation_1-logloss:0.90794
[4999]	validation_0-logloss:0.33665	validation_1-logloss:0.55554
[1246]	validation_0-logloss:0.03756	validation_1-logloss:0.90807
[1247]	validation_0-logloss:0.03752	validation_1-logloss:0.90839
[1248]	validation_0-logloss:0.03750	validation_1-logloss:0.90865
[1249]	validation_0-logloss:0.03747	validation_1-logloss:0.90888
[1250]	validation_0-logloss:0.03742	validation_1-logloss:0.90907
[1251]	validation_0-logloss:0.03741	validation_1-logloss:0.90919
[1252]	validation_0-logloss:0.03738	validation_1-logloss:0.90922
[1253]	validation_0-logloss:0.03734	validation_1-logloss:0.90965

[I 2026-05-05 06:16:23,204] Trial 2 finished with value: 0.7369039971017992 and parameters: {'max_depth': 4, 'min_child_weight': 7, 'reg_alpha': 2.241381331047004, 'reg_lambda': 4.476934714115417e-08, 'gamma': 0.00011286190232422962, 'subsample': 0.7252833359646541, 'colsample_bytree': 0.5585484713001434, 'colsample_bylevel': 0.8859256746891728, 'learning_rate': 0.021820651758079585, 'max_bin': 256}. Best is trial 2 with value: 0.7369039971017992.

[1254]	validation_0-logloss:0.03730	validation_1-logloss:0.90997
[1255]	validation_0-logloss:0.03725	validation_1-logloss:0.91007
[1256]	validation_0-logloss:0.03723	validation_1-logloss:0.91027
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[4853]	validation_0-logloss:0.01387	validation_1-logloss:1.15242
[4854]	validation_0-logloss:0.01387	validation_1-logloss:1.15243
[4855]	validation_0-logloss:0.01387	validation_1-logloss:1.15243
[4856]	validation_0-logloss:0.01387	validation_1-logloss:1.15236
[4857]	validation_0-logloss:0.01387	validation_1-logloss:1.15239
[4858]	validation_0-logloss:0.01387	validation_1-logloss:1.15244
[4859]	validation_0-logloss:0.01387	validation_1-logloss:1.15243
[4860]	validation_0-logloss:0.01387	validation_1-logloss:1.15242
[4861]	validation_0-logloss:0.01387	validation_1-logloss:1.15246
[4862]	validation_0-logloss:0.01387	validation_1-logloss:1.15246
[4863]	validation_0-logloss:0.01387	validation_1-logloss:1.15248
[4864]	validation_0-logloss:0.01386	validation_1-logloss:1.15253
[4865]	validation_0-logloss:0.01386	validation_1-logloss:1.15249
[4866]	validation_0-logloss:0.01386	validation_1-logloss:1.15255
[4867]	validation_0-logloss:0.01386	validation_1-logloss:1.15259
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[4869]	validation_0-logloss:0.01386	validation_1-logloss:1.15259
[4870]	validation_0-logloss:0.01386	validation_1-logloss:1.15263
[4871]	validation_0-logloss:0.01386	validation_1-logloss:1.15263
[4872]	validation_0-logloss:0.01385	validation_1-logloss:1.15269
[4873]	validation_0-logloss:0.01385	validation_1-logloss:1.15271
[4874]	validation_0-logloss:0.01385	validation_1-logloss:1.15271
[4875]	validation_0-logloss:0.01385	validation_1-logloss:1.15271
[4876]	validation_0-logloss:0.01385	validation_1-logloss:1.15276
[4877]	validation_0-logloss:0.01385	validation_1-logloss:1.15280
[4878]	validation_0-logloss:0.01385	validation_1-logloss:1.15284
[4879]	validation_0-logloss:0.01385	validation_1-logloss:1.15288
[4880]	validation_0-logloss:0.01385	validation_1-logloss:1.15288
[4881]	validation_0-logloss:0.01384	validation_1-logloss:1.15289
[4882]	validation_0-logloss:0.01384	validation_1-logloss:1.15288
[4883]	validation_0-logloss:0.01384	validation_1-logloss:1.15289
[4884]	validation_0-logloss:0.01384	validation_1-logloss:1.15291
[4885]	validation_0-logloss:0.01384	validation_1-logloss:1.15300
[4886]	validation_0-logloss:0.01384	validation_1-logloss:1.15295
[4887]	validation_0-logloss:0.01384	validation_1-logloss:1.15303
[4888]	validation_0-logloss:0.01384	validation_1-logloss:1.15303
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[4890]	validation_0-logloss:0.01383	validation_1-logloss:1.15304
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[4892]	validation_0-logloss:0.01383	validation_1-logloss:1.15315
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[4900]	validation_0-logloss:0.01382	validation_1-logloss:1.15329
[4901]	validation_0-logloss:0.01382	validation_1-logloss:1.15333
[4902]	validation_0-logloss:0.01382	validation_1-logloss:1.15327
[4903]	validation_0-logloss:0.01381	validation_1-logloss:1.15326
[4904]	validation_0-logloss:0.01381	validation_1-logloss:1.15344
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[4906]	validation_0-logloss:0.01381	validation_1-logloss:1.15343
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[4908]	validation_0-logloss:0.01381	validation_1-logloss:1.15345
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[4911]	validation_0-logloss:0.01380	validation_1-logloss:1.15366
[4912]	validation_0-logloss:0.01380	validation_1-logloss:1.15361
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[4914]	validation_0-logloss:0.01380	validation_1-logloss:1.15374
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[4932]	validation_0-logloss:0.01377	validation_1-logloss:1.15441
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[4934]	validation_0-logloss:0.01377	validation_1-logloss:1.15449
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[4938]	validation_0-logloss:0.01376	validation_1-logloss:1.15465
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[4946]	validation_0-logloss:0.01375	validation_1-logloss:1.15479
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[4952]	validation_0-logloss:0.01374	validation_1-logloss:1.15494
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[4954]	validation_0-logloss:0.01374	validation_1-logloss:1.15493
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[4981]	validation_0-logloss:0.01372	validation_1-logloss:1.15534
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[4983]	validation_0-logloss:0.01372	validation_1-logloss:1.15529
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[4985]	validation_0-logloss:0.01372	validation_1-logloss:1.15535
[4986]	validation_0-logloss:0.01372	validation_1-logloss:1.15541
[4987]	validation_0-logloss:0.01372	validation_1-logloss:1.15534
[4988]	validation_0-logloss:0.01372	validation_1-logloss:1.15534
[4989]	validation_0-logloss:0.01371	validation_1-logloss:1.15535
[4990]	validation_0-logloss:0.01371	validation_1-logloss:1.15538
[4991]	validation_0-logloss:0.01371	validation_1-logloss:1.15541
[4992]	validation_0-logloss:0.01371	validation_1-logloss:1.15552
[4993]	validation_0-logloss:0.01371	validation_1-logloss:1.15554
[4994]	validation_0-logloss:0.01371	validation_1-logloss:1.15552
[4995]	validation_0-logloss:0.01370	validation_1-logloss:1.15555
[4996]	validation_0-logloss:0.01370	validation_1-logloss:1.15556
[4997]	validation_0-logloss:0.01370	validation_1-logloss:1.15566
[4998]	validation_0-logloss:0.01370	validation_1-logloss:1.15573
[4999]	validation_0-logloss:0.01369	validation_1-logloss:1.15575

[I 2026-05-05 06:17:24,392] Trial 1 finished with value: 0.7272262609185686 and parameters: {'max_depth': 7, 'min_child_weight': 3, 'reg_alpha': 0.14369796212557015, 'reg_lambda': 0.00030482460930186133, 'gamma': 1.7623057914557492e-05, 'subsample': 0.9875778800134174, 'colsample_bytree': 0.6383784380302511, 'colsample_bylevel': 0.6163564931143626, 'learning_rate': 0.17239270546359142, 'max_bin': 128}. Best is trial 2 with value: 0.7369039971017992.
[I 2026-05-05 06:17:25,344] A new study created in memory with name: shipping_delay_random_forest

  CV AUC: 0.7369  |  Threshold: 0.585

──────────────────────────────────────────────────  RANDOM_FOREST

[I 2026-05-05 06:17:39,907] Trial 0 finished with value: 0.7354277663728213 and parameters: {'n_estimators': 250, 'max_depth': 8, 'min_samples_split': 18, 'min_samples_leaf': 6, 'max_features': 0.7, 'bootstrap': True, 'class_weight': 'balanced_subsample'}. Best is trial 0 with value: 0.7354277663728213.
[I 2026-05-05 06:17:41,440] Trial 2 finished with value: 0.7308579479128929 and parameters: {'n_estimators': 300, 'max_depth': 28, 'min_samples_split': 7, 'min_samples_leaf': 7, 'max_features': 0.7, 'bootstrap': False, 'class_weight': 'balanced'}. Best is trial 0 with value: 0.7354277663728213.
[I 2026-05-05 06:17:42,360] Trial 1 finished with value: 0.7320776073743107 and parameters: {'n_estimators': 450, 'max_depth': 26, 'min_samples_split': 16, 'min_samples_leaf': 8, 'max_features': 0.5, 'bootstrap': False, 'class_weight': None}. Best is trial 0 with value: 0.7354277663728213.
[I 2026-05-05 06:17:45,905] A new study created in memory with name: shipping_delay_logistic_regression

  CV AUC: 0.7354  |  Threshold: 0.369

──────────────────────────────────────────────────  LOGISTIC_REGRESSION

[I 2026-05-05 06:17:46,309] Trial 1 finished with value: 0.7194047417783681 and parameters: {'penalty_solver': 'l1_saga', 'C': 0.09575334507574831}. Best is trial 1 with value: 0.7194047417783681.
[I 2026-05-05 06:17:46,457] Trial 2 finished with value: 0.7193272954152076 and parameters: {'penalty_solver': 'elasticnet_saga', 'C': 0.1375082115662957, 'l1_ratio': 0.9494909700048194}. Best is trial 1 with value: 0.7194047417783681.
[I 2026-05-05 06:17:47,154] Trial 0 finished with value: 0.7191757839230366 and parameters: {'penalty_solver': 'l2_saga', 'C': 0.535667768221033}. Best is trial 1 with value: 0.7194047417783681.

  CV AUC: 0.7194  |  Threshold: 0.712

All architectures trained!

In [12]:

leaderboard = pd.DataFrame([
    {
        "Architecture"      : arch.upper(),
        "CV AUC"            : round(state.best_score, 4),
        "Optimal Threshold" : round(state.optimal_threshold or 0.5, 3),
        "Train Time (s)"    : round(state.training_time_seconds, 1),
    }
    for arch, state in model_states.items()
]).sort_values("CV AUC", ascending=False).reset_index(drop=True)

leaderboard.index += 1  # 1-based ranking

print("\n" + "=" * 60)
print("  ARCHITECTURE LEADERBOARD")
print("=" * 60)
display(leaderboard)

best_arch = leaderboard.iloc[0]['Architecture'].lower()
print(f"\nWinner: {best_arch.upper()} — proceeding with LGBM for detailed analysis (SHAP available).")

leaderboard = pd.DataFrame([ { "Architecture" : arch.upper(), "CV AUC" : round(state.best_score, 4), "Optimal Threshold" : round(state.optimal_threshold or 0.5, 3), "Train Time (s)" : round(state.training_time_seconds, 1), } for arch, state in model_states.items() ]).sort_values("CV AUC", ascending=False).reset_index(drop=True) leaderboard.index += 1 # 1-based ranking print("\n" + "=" * 60) print(" ARCHITECTURE LEADERBOARD") print("=" * 60) display(leaderboard) best_arch = leaderboard.iloc[0]['Architecture'].lower() print(f"\nWinner: {best_arch.upper()} — proceeding with LGBM for detailed analysis (SHAP available).")

============================================================
  ARCHITECTURE LEADERBOARD
============================================================

	Architecture	CV AUC	Optimal Threshold	Train Time (s)
1	LGBM	0.7426	0.498	111.3
2	XGB	0.7369	0.585	193.0
3	RANDOM_FOREST	0.7354	0.369	20.6
4	LOGISTIC_REGRESSION	0.7194	0.712	1.3

Winner: LGBM — proceeding with LGBM for detailed analysis (SHAP available).

8. Comprehensive Test Set Evaluation¶

This is the moment of truth.

Cross-validation scores tell you how well the model generalised during training. The hold-out test set — never seen by any fitting step, including the transform pipeline — gives you an unbiased estimate of real-world performance. No optimism, no leakage.

We use bitbullet.evaluate.ModelEvaluator for the notebook diagnostics and evaluate_classification for a JSON-ready metrics report that can be attached directly to model metadata. The threshold applied is the one discovered by Youden's Index during training — not a hand-tuned 0.5.

In [13]:

# Score the sealed test set
production_model   = lgbm_state.model
y_pred_proba_test  = production_model.predict_proba(X_test_transformed)[:, 1]

eval_config = EvaluationConfig(
    name="shipping_delay_test_eval",
    threshold=lgbm_state.optimal_threshold,  # Youden's J threshold from training
    optimize_threshold=False,
    generate_plots=False,                    # We produce inline plots below
    generate_report=False,
    verbose=False
)

evaluator    = ModelEvaluator(config=eval_config)
test_results = evaluator.evaluate(
    y_true=y_test.values,
    y_pred_proba=y_pred_proba_test
)

print(test_results)

# Score the sealed test set production_model = lgbm_state.model y_pred_proba_test = production_model.predict_proba(X_test_transformed)[:, 1] eval_config = EvaluationConfig( name="shipping_delay_test_eval", threshold=lgbm_state.optimal_threshold, # Youden's J threshold from training optimize_threshold=False, generate_plots=False, # We produce inline plots below generate_report=False, verbose=False ) evaluator = ModelEvaluator(config=eval_config) test_results = evaluator.evaluate( y_true=y_test.values, y_pred_proba=y_pred_proba_test ) print(test_results)

[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877
[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07
[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796
[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07
[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2
============================================================
Evaluation Results
============================================================
Threshold  : 0.4984

Classification metrics:
  Accuracy     : 0.6582
  Precision    : 0.7913
  Recall       : 0.5804
  F1           : 0.6696
  Specificity  : 0.7734

Probabilistic metrics:
  ROC AUC      : 0.7322
  PR AUC       : 0.8479
  Cohen's κ    : 0.3325

Confusion matrix:
  TN    686   FP    201
  FN    551   TP    762
============================================================

In [14]:

# ─── Evaluation visualisation ────────────────────────────────────────────────
fig, axes = plt.subplots(2, 2, figsize=(14, 11))
fig.suptitle("Shipping Delay Predictor — Hold-Out Test Set Evaluation",
             fontsize=14, fontweight="bold", y=1.01)

BLUE, RED, GREY = "#2563EB", "#DC2626", "#9CA3AF"

# 1. Confusion matrix
ax = axes[0, 0]
cm = test_results.confusion_matrix
sns.heatmap(
    cm, annot=True, fmt='d', cmap='Blues', ax=ax, cbar=False, linewidths=0.5,
    xticklabels=['Predicted\nDelayed', 'Predicted\nOn Time'],
    yticklabels=['Actual\nDelayed', 'Actual\nOn Time']
)
ax.set_title(f"Confusion Matrix  (τ = {test_results.threshold:.3f})", fontweight="bold")

# 2. ROC curve
ax = axes[0, 1]
fpr_arr, tpr_arr, _ = roc_curve(y_test, y_pred_proba_test)
ax.plot(fpr_arr, tpr_arr, color=BLUE, lw=2, label=f'ROC AUC = {test_results.roc_auc:.4f}')
ax.plot([0, 1], [0, 1], color=GREY, ls='--', lw=1, label='Random')
ax.scatter([test_results.fpr], [test_results.sensitivity],
           color=RED, s=80, zorder=5, label="Operating point (Youden's J)")
ax.set(xlabel='False Positive Rate', ylabel='True Positive Rate', title='ROC Curve')
ax.set_title('ROC Curve', fontweight="bold")
ax.legend(loc='lower right', fontsize=9)
ax.grid(True, alpha=0.3)

# 3. Precision-Recall curve
ax = axes[1, 0]
prec_arr, rec_arr, _ = precision_recall_curve(y_test, y_pred_proba_test)
baseline_pr = float(y_test.mean())
ax.plot(rec_arr, prec_arr, color=BLUE, lw=2, label=f'PR AUC = {test_results.pr_auc:.4f}')
ax.axhline(baseline_pr, color=GREY, ls='--', lw=1,
           label=f'Baseline (prevalence = {baseline_pr:.2f})')
ax.set(xlabel='Recall', ylabel='Precision', title='Precision-Recall Curve')
ax.set_title('Precision-Recall Curve', fontweight="bold")
ax.legend(loc='upper right', fontsize=9)
ax.grid(True, alpha=0.3)

# 4. Score distributions
ax = axes[1, 1]
pos_scores = y_pred_proba_test[y_test.values == 1]
neg_scores = y_pred_proba_test[y_test.values == 0]
n_bins = max(30, int(np.sqrt(len(y_pred_proba_test))))
ax.hist(pos_scores, bins=n_bins, color=BLUE, alpha=0.55, density=True,
        label=f'On Time  (n={len(pos_scores):,})')
ax.hist(neg_scores, bins=n_bins, color=RED, alpha=0.55, density=True,
        label=f'Delayed  (n={len(neg_scores):,})')
ax.axvline(test_results.threshold, color='black', ls='--', lw=2,
           label=f'Threshold = {test_results.threshold:.3f}')
ax.set(xlabel='P(On Time)', ylabel='Density', title='Score Distributions by True Class')
ax.set_title('Score Distributions by True Class', fontweight="bold")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

print(f"\n  ROC AUC  {test_results.roc_auc:.4f}   PR AUC  {test_results.pr_auc:.4f}")
print(f"  F1       {test_results.f1_score:.4f}   κ (Kappa) {test_results.kappa:.4f}")
print(f"  Sens.    {test_results.sensitivity:.4f}   Spec.     {test_results.specificity:.4f}")

# ─── Evaluation visualisation ──────────────────────────────────────────────── fig, axes = plt.subplots(2, 2, figsize=(14, 11)) fig.suptitle("Shipping Delay Predictor — Hold-Out Test Set Evaluation", fontsize=14, fontweight="bold", y=1.01) BLUE, RED, GREY = "#2563EB", "#DC2626", "#9CA3AF" # 1. Confusion matrix ax = axes[0, 0] cm = test_results.confusion_matrix sns.heatmap( cm, annot=True, fmt='d', cmap='Blues', ax=ax, cbar=False, linewidths=0.5, xticklabels=['Predicted\nDelayed', 'Predicted\nOn Time'], yticklabels=['Actual\nDelayed', 'Actual\nOn Time'] ) ax.set_title(f"Confusion Matrix (τ = {test_results.threshold:.3f})", fontweight="bold") # 2. ROC curve ax = axes[0, 1] fpr_arr, tpr_arr, _ = roc_curve(y_test, y_pred_proba_test) ax.plot(fpr_arr, tpr_arr, color=BLUE, lw=2, label=f'ROC AUC = {test_results.roc_auc:.4f}') ax.plot([0, 1], [0, 1], color=GREY, ls='--', lw=1, label='Random') ax.scatter([test_results.fpr], [test_results.sensitivity], color=RED, s=80, zorder=5, label="Operating point (Youden's J)") ax.set(xlabel='False Positive Rate', ylabel='True Positive Rate', title='ROC Curve') ax.set_title('ROC Curve', fontweight="bold") ax.legend(loc='lower right', fontsize=9) ax.grid(True, alpha=0.3) # 3. Precision-Recall curve ax = axes[1, 0] prec_arr, rec_arr, _ = precision_recall_curve(y_test, y_pred_proba_test) baseline_pr = float(y_test.mean()) ax.plot(rec_arr, prec_arr, color=BLUE, lw=2, label=f'PR AUC = {test_results.pr_auc:.4f}') ax.axhline(baseline_pr, color=GREY, ls='--', lw=1, label=f'Baseline (prevalence = {baseline_pr:.2f})') ax.set(xlabel='Recall', ylabel='Precision', title='Precision-Recall Curve') ax.set_title('Precision-Recall Curve', fontweight="bold") ax.legend(loc='upper right', fontsize=9) ax.grid(True, alpha=0.3) # 4. Score distributions ax = axes[1, 1] pos_scores = y_pred_proba_test[y_test.values == 1] neg_scores = y_pred_proba_test[y_test.values == 0] n_bins = max(30, int(np.sqrt(len(y_pred_proba_test)))) ax.hist(pos_scores, bins=n_bins, color=BLUE, alpha=0.55, density=True, label=f'On Time (n={len(pos_scores):,})') ax.hist(neg_scores, bins=n_bins, color=RED, alpha=0.55, density=True, label=f'Delayed (n={len(neg_scores):,})') ax.axvline(test_results.threshold, color='black', ls='--', lw=2, label=f'Threshold = {test_results.threshold:.3f}') ax.set(xlabel='P(On Time)', ylabel='Density', title='Score Distributions by True Class') ax.set_title('Score Distributions by True Class', fontweight="bold") ax.legend(fontsize=9) ax.grid(True, alpha=0.3) plt.tight_layout() plt.show() print(f"\n ROC AUC {test_results.roc_auc:.4f} PR AUC {test_results.pr_auc:.4f}") print(f" F1 {test_results.f1_score:.4f} κ (Kappa) {test_results.kappa:.4f}") print(f" Sens. {test_results.sensitivity:.4f} Spec. {test_results.specificity:.4f}")

  ROC AUC  0.7322   PR AUC  0.8479
  F1       0.6696   κ (Kappa) 0.3325
  Sens.    0.5804   Spec.     0.7734

9. Model Serialisation with Full Provenance¶

Saving a model file is not enough. Production systems need to answer: What features was this model trained on? What were its hyperparameters? What test-set metrics did it achieve? Who trained it and when?

ModelMetadata captures all of that, plus the fitted feature schema, preprocessing pointer, search metadata, target mapping, and a lightweight inference contract. ModelSerializer bundles it with the model object and optionally the training/test datasets into a single, self-describing package — everything needed for audit, rollback, debugging, and exact inference replay.

Because we evaluated first (Section 8), we can embed the test metrics directly into the metadata before saving.

In [15]:

metadata = ModelMetadata(
    name="shipping_delay_predictor_v1",
    version="1.0.0",
    model_type="LGBMClassifier",
    framework="lightgbm",
    task="binary_classification",
    trained_at=datetime.now(),
    feature_names=lgbm_state.feature_names,
    n_features=lgbm_state.n_features_selected,
    hyperparameters=lgbm_state.best_params,
    optimal_threshold=lgbm_state.optimal_threshold,
    training_time_seconds=lgbm_state.training_time_seconds,
    tags=["binary_classification", "logistics", "lightgbm", "v1"],
    notes="Trained on e-commerce shipping data. 80/20 stratified split. Youden threshold."
)

# CV performance from the best Optuna trial
if lgbm_state.study:
    fold_scores = lgbm_state.study.best_trial.user_attrs.get('cv_scores', [lgbm_state.best_score])
    metadata.add_cv_scores("roc_auc", fold_scores)

# Test-set performance — locked in from Section 8
metadata.add_metric("test_roc_auc",  test_results.roc_auc)
metadata.add_metric("test_pr_auc",   test_results.pr_auc)
metadata.add_metric("test_f1",       test_results.f1_score)
metadata.add_metric("test_accuracy", test_results.accuracy)
metadata.add_metric("test_kappa",    test_results.kappa)

# Dataset provenance
metadata.add_dataset(X_train_transformed, y_train, "train")
metadata.add_dataset(X_test_transformed,  y_test,  "test")

print(metadata.summary())

metadata = ModelMetadata( name="shipping_delay_predictor_v1", version="1.0.0", model_type="LGBMClassifier", framework="lightgbm", task="binary_classification", trained_at=datetime.now(), feature_names=lgbm_state.feature_names, n_features=lgbm_state.n_features_selected, hyperparameters=lgbm_state.best_params, optimal_threshold=lgbm_state.optimal_threshold, training_time_seconds=lgbm_state.training_time_seconds, tags=["binary_classification", "logistics", "lightgbm", "v1"], notes="Trained on e-commerce shipping data. 80/20 stratified split. Youden threshold." ) # CV performance from the best Optuna trial if lgbm_state.study: fold_scores = lgbm_state.study.best_trial.user_attrs.get('cv_scores', [lgbm_state.best_score]) metadata.add_cv_scores("roc_auc", fold_scores) # Test-set performance — locked in from Section 8 metadata.add_metric("test_roc_auc", test_results.roc_auc) metadata.add_metric("test_pr_auc", test_results.pr_auc) metadata.add_metric("test_f1", test_results.f1_score) metadata.add_metric("test_accuracy", test_results.accuracy) metadata.add_metric("test_kappa", test_results.kappa) # Dataset provenance metadata.add_dataset(X_train_transformed, y_train, "train") metadata.add_dataset(X_test_transformed, y_test, "test") print(metadata.summary())

Model: shipping_delay_predictor_v1 (v1.0.0)
Type: LGBMClassifier (lightgbm)
Task: binary_classification
Features: 19
Trained: 2026-05-05 06:35

Performance Metrics:
  test_roc_auc: 0.7322
  test_pr_auc: 0.8479
  test_f1: 0.6696
  test_accuracy: 0.6582
  test_kappa: 0.3325

Cross-Validation:
  roc_auc: 0.7426 ± 0.0130

Training Data: 8799 samples
Test Data: 2200 samples

In [16]:

model_path = "models/shipping_delay_predictor_v1.pkl"

ModelSerializer.save(
    model=lgbm_state.model,
    path=model_path,
    metadata=metadata,
    train_data=(X_train_transformed, y_train),
    test_data=(X_test_transformed, y_test),
    include_datasets=True
)
print(f"Model package  → {model_path}")
print(f"Metadata JSON  → models/shipping_delay_predictor_v1_metadata.json")

# Verify the round-trip
package = ModelSerializer.load(model_path)
print(f"\nPackage loaded:")
print(package.summary())

model_path = "models/shipping_delay_predictor_v1.pkl" ModelSerializer.save( model=lgbm_state.model, path=model_path, metadata=metadata, train_data=(X_train_transformed, y_train), test_data=(X_test_transformed, y_test), include_datasets=True ) print(f"Model package → {model_path}") print(f"Metadata JSON → models/shipping_delay_predictor_v1_metadata.json") # Verify the round-trip package = ModelSerializer.load(model_path) print(f"\nPackage loaded:") print(package.summary())

Model package  → models/shipping_delay_predictor_v1.pkl
Metadata JSON  → models/shipping_delay_predictor_v1_metadata.json

Package loaded:
Model: shipping_delay_predictor_v1 (v1.0.0)
Type: LGBMClassifier (lightgbm)
Task: binary_classification
Features: 19
Trained: 2026-05-05 06:35

Performance Metrics:
  test_roc_auc: 0.7322
  test_pr_auc: 0.8479
  test_f1: 0.6696
  test_accuracy: 0.6582
  test_kappa: 0.3325

Cross-Validation:
  roc_auc: 0.7426 ± 0.0130

Training Data: 8799 samples
Test Data: 2200 samples

Training data included: 8799 samples
Test data included: 2200 samples

10. Explainability with SHAP¶

Because we set generate_shap=True in TrainConfig, the trainer automatically computed SHAP values during the training run and stored them in lgbm_state. No separate explainer setup, no additional data passing — the results are waiting.

We compare native tree feature importance (based on information gain split counts) against SHAP mean |value| (a theoretically grounded Shapley attribution). Divergences reveal features frequently used by the tree but with disproportionately small marginal contributions — candidates for removal in a future feature engineering cycle.

In [17]:

fig, axes = plt.subplots(1, 2, figsize=(14, 6))
fig.suptitle("Feature Importance — Native Tree vs SHAP", fontsize=13, fontweight="bold")

# Native tree importance
ax = axes[0]
if lgbm_state.feature_importance is not None:
    top_tree = lgbm_state.feature_importance.head(12)
    ax.barh(top_tree['feature'][::-1], top_tree['importance'][::-1], color="#3B82F6")
    ax.set_title("Native Tree Importance (top 12)", fontweight="bold")
    ax.set_xlabel("Importance Score")
    ax.grid(axis='x', alpha=0.3)
else:
    ax.text(0.5, 0.5, "Not available", ha='center', va='center', transform=ax.transAxes)

# SHAP importance
ax = axes[1]
if lgbm_state.shap_feature_importance is not None:
    top_shap = lgbm_state.shap_feature_importance.head(12)
    ax.barh(top_shap['feature'][::-1], top_shap['importance'][::-1], color="#10B981")
    ax.set_title("SHAP Mean |Value| (top 12)", fontweight="bold")
    ax.set_xlabel("Mean |SHAP value|")
    ax.grid(axis='x', alpha=0.3)
else:
    ax.text(0.5, 0.5, "SHAP not computed\n(set generate_shap=True)",
            ha='center', va='center', transform=ax.transAxes)

plt.tight_layout()
plt.show()

# Rank comparison — where do the two methods agree and diverge?
if lgbm_state.feature_importance is not None and lgbm_state.shap_feature_importance is not None:
    tree_rank = lgbm_state.feature_importance.assign(
        tree_rank=range(1, len(lgbm_state.feature_importance) + 1)
    ).rename(columns={'importance': 'tree_importance'})

    shap_rank = lgbm_state.shap_feature_importance.assign(
        shap_rank=range(1, len(lgbm_state.shap_feature_importance) + 1)
    ).rename(columns={'importance': 'shap_importance'})

    comparison = (
        tree_rank.merge(shap_rank, on='feature')
        .assign(rank_delta=lambda d: (d['tree_rank'] - d['shap_rank']).abs())
        .sort_values('shap_rank')
        .head(12)
    )
    print("\nFeature ranking comparison (sorted by SHAP rank):")
    display(comparison[['feature', 'tree_rank', 'shap_rank', 'rank_delta']])

fig, axes = plt.subplots(1, 2, figsize=(14, 6)) fig.suptitle("Feature Importance — Native Tree vs SHAP", fontsize=13, fontweight="bold") # Native tree importance ax = axes[0] if lgbm_state.feature_importance is not None: top_tree = lgbm_state.feature_importance.head(12) ax.barh(top_tree['feature'][::-1], top_tree['importance'][::-1], color="#3B82F6") ax.set_title("Native Tree Importance (top 12)", fontweight="bold") ax.set_xlabel("Importance Score") ax.grid(axis='x', alpha=0.3) else: ax.text(0.5, 0.5, "Not available", ha='center', va='center', transform=ax.transAxes) # SHAP importance ax = axes[1] if lgbm_state.shap_feature_importance is not None: top_shap = lgbm_state.shap_feature_importance.head(12) ax.barh(top_shap['feature'][::-1], top_shap['importance'][::-1], color="#10B981") ax.set_title("SHAP Mean |Value| (top 12)", fontweight="bold") ax.set_xlabel("Mean |SHAP value|") ax.grid(axis='x', alpha=0.3) else: ax.text(0.5, 0.5, "SHAP not computed\n(set generate_shap=True)", ha='center', va='center', transform=ax.transAxes) plt.tight_layout() plt.show() # Rank comparison — where do the two methods agree and diverge? if lgbm_state.feature_importance is not None and lgbm_state.shap_feature_importance is not None: tree_rank = lgbm_state.feature_importance.assign( tree_rank=range(1, len(lgbm_state.feature_importance) + 1) ).rename(columns={'importance': 'tree_importance'}) shap_rank = lgbm_state.shap_feature_importance.assign( shap_rank=range(1, len(lgbm_state.shap_feature_importance) + 1) ).rename(columns={'importance': 'shap_importance'}) comparison = ( tree_rank.merge(shap_rank, on='feature') .assign(rank_delta=lambda d: (d['tree_rank'] - d['shap_rank']).abs()) .sort_values('shap_rank') .head(12) ) print("\nFeature ranking comparison (sorted by SHAP rank):") display(comparison[['feature', 'tree_rank', 'shap_rank', 'rank_delta']])

Feature ranking comparison (sorted by SHAP rank):

	feature	tree_rank	shap_rank	rank_delta
2	Discount_offered	3	1	2
1	Weight_in_gms	2	2	0
5	Prior_purchases	6	3	3
0	Cost_of_the_Product	1	4	3
3	Customer_rating	4	5	1
4	Customer_care_calls	5	6	1
7	Product_importance_medium	8	7	1
10	Product_importance_high	11	8	3
13	Warehouse_block_A	14	9	5
8	Gender_F	9	10	1
15	Mode_of_Shipment_Flight	16	11	5
14	Product_importance_low	15	12	3

11. Formal Training Report¶

TrainingReportGenerator consolidates the Optuna study, trainer state, and configuration into a structured, printable report — suitable for model cards, peer review, or automated audit trails. It also surfaces hyperparameter importance via fANOVA: which hyperparameters moved the needle most during the search.

In [18]:

report = TrainingReportGenerator.from_trainer_state(
    trainer_state=lgbm_state,
    config=lgbm_config,
    study=lgbm_state.study
)

print(report)

# Which hyperparameters actually drove performance?
if lgbm_state.study:
    print("\n" + "=" * 80)
    print("HYPERPARAMETER IMPORTANCE  (fANOVA — higher = greater impact on AUC)")
    print("=" * 80)
    hp_imp = TrainingReportGenerator.generate_optuna_importance_report(
        lgbm_state.study, top_n=8
    )
    if not hp_imp.empty:
        display(hp_imp)
    else:
        print("(Needs ≥ 3 completed trials for fANOVA)")

report = TrainingReportGenerator.from_trainer_state( trainer_state=lgbm_state, config=lgbm_config, study=lgbm_state.study ) print(report) # Which hyperparameters actually drove performance? if lgbm_state.study: print("\n" + "=" * 80) print("HYPERPARAMETER IMPORTANCE (fANOVA — higher = greater impact on AUC)") print("=" * 80) hp_imp = TrainingReportGenerator.generate_optuna_importance_report( lgbm_state.study, top_n=8 ) if not hp_imp.empty: display(hp_imp) else: print("(Needs ≥ 3 completed trials for fANOVA)")

================================================================================
Training Report: shipping_delay_lgbm
================================================================================

Model Configuration:
  Type: lgbm
  Task: binary_classification

Optimization Results:
  Best CV Score: 0.742580 ± 0.000000
  CV Scores: []
  Best Iteration: 0
  Total Trials: 5

Best Hyperparameters:
  num_leaves: 125
  max_depth: 7
  min_child_samples: 53
  lambda_l1: 0.000001
  lambda_l2: 0.000001
  min_gain_to_split: 0.168427
  feature_fraction: 0.946029
  bagging_fraction: 0.564919
  bagging_freq: 2
  learning_rate: 0.091531
  max_bin: 63
  n_estimators: 48

Threshold Optimization:
  Optimal Threshold: 0.4984
  youden_index: 0.5163
  tpr: 0.6636
  fpr: 0.1474
  sensitivity: 0.6636
  specificity: 0.8526

Feature Selection:
  Original Features: 19
  Selected Features: 19
  Reduction: 0.0%
================================================================================

================================================================================
HYPERPARAMETER IMPORTANCE  (fANOVA — higher = greater impact on AUC)
================================================================================

	parameter	importance
0	max_depth	0.187103
1	bagging_fraction	0.178320
2	num_leaves	0.169172
3	bagging_freq	0.119174
4	max_bin	0.111460
5	min_child_samples	0.071306
6	learning_rate	0.065942
7	lambda_l1	0.033099

12. Production Inference — The Complete End-to-End Pipeline¶

In a real deployment, the training environment and the inference service are completely separate processes. The inference service needs exactly two artefacts — the serialised pipeline and the model package — and must apply them in the correct order.

The cell below simulates what your inference microservice executes on a batch of new shipments arriving in production:

In [19]:

# ── Step 1: Load artefacts from disk ─────────────────────────────────────────
inference_pipeline = TransformPipeline.load("pipeline_binary.pkl")
loaded_package     = ModelSerializer.load("models/shipping_delay_predictor_v1.pkl")
inference_model    = loaded_package.model
threshold          = loaded_package.metadata.optimal_threshold

print(f"Pipeline loaded — fitted : {inference_pipeline.is_fitted}")
print(f"Model loaded    — name   : {loaded_package.metadata.name} v{loaded_package.metadata.version}")
print(f"Decision threshold       : {threshold:.4f}")

# ── Step 2: Simulate incoming raw records ─────────────────────────────────────
# In production these would be live records. We use held-out test rows as a proxy.
new_shipments_raw = X_test.iloc[:6].copy()

# ── Step 3: Apply the serialised transform pipeline ──────────────────────────
new_shipments_transformed = inference_pipeline.transform(new_shipments_raw)

# ── Step 4: Score and apply threshold ────────────────────────────────────────
on_time_scores = inference_model.predict_proba(new_shipments_transformed)[:, 1]
predictions    = (on_time_scores >= threshold).astype(int)

# ── Step 5: Format output ─────────────────────────────────────────────────────
results_df = new_shipments_raw.assign(
    on_time_probability=on_time_scores.round(4),
    prediction=predictions,
    status=pd.Series(predictions).map({1: 'On Time', 0: 'Delayed'}).values,
    ground_truth=y_test.iloc[:6].values
)

print("\n" + "=" * 60)
print("  INFERENCE RESULTS")
print("=" * 60)
display(
    results_df[[
        'Warehouse_block', 'Mode_of_Shipment', 'Weight_in_gms',
        'on_time_probability', 'status', 'ground_truth'
    ]]
)

print("\nInference complete: two artefacts, zero leakage, full reproducibility.")

# ── Step 1: Load artefacts from disk ───────────────────────────────────────── inference_pipeline = TransformPipeline.load("pipeline_binary.pkl") loaded_package = ModelSerializer.load("models/shipping_delay_predictor_v1.pkl") inference_model = loaded_package.model threshold = loaded_package.metadata.optimal_threshold print(f"Pipeline loaded — fitted : {inference_pipeline.is_fitted}") print(f"Model loaded — name : {loaded_package.metadata.name} v{loaded_package.metadata.version}") print(f"Decision threshold : {threshold:.4f}") # ── Step 2: Simulate incoming raw records ───────────────────────────────────── # In production these would be live records. We use held-out test rows as a proxy. new_shipments_raw = X_test.iloc[:6].copy() # ── Step 3: Apply the serialised transform pipeline ────────────────────────── new_shipments_transformed = inference_pipeline.transform(new_shipments_raw) # ── Step 4: Score and apply threshold ──────────────────────────────────────── on_time_scores = inference_model.predict_proba(new_shipments_transformed)[:, 1] predictions = (on_time_scores >= threshold).astype(int) # ── Step 5: Format output ───────────────────────────────────────────────────── results_df = new_shipments_raw.assign( on_time_probability=on_time_scores.round(4), prediction=predictions, status=pd.Series(predictions).map({1: 'On Time', 0: 'Delayed'}).values, ground_truth=y_test.iloc[:6].values ) print("\n" + "=" * 60) print(" INFERENCE RESULTS") print("=" * 60) display( results_df[[ 'Warehouse_block', 'Mode_of_Shipment', 'Weight_in_gms', 'on_time_probability', 'status', 'ground_truth' ]] ) print("\nInference complete: two artefacts, zero leakage, full reproducibility.")

Pipeline loaded — fitted : True
Model loaded    — name   : shipping_delay_predictor_v1 v1.0.0
Decision threshold       : 0.4984
[LightGBM] [Warning] feature_fraction is set=0.9460292540594877, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9460292540594877
[LightGBM] [Warning] lambda_l2 is set=7.259104745433034e-07, reg_lambda=0.0 will be ignored. Current value: lambda_l2=7.259104745433034e-07
[LightGBM] [Warning] min_gain_to_split is set=0.16842722147088796, min_split_gain=0.0 will be ignored. Current value: min_gain_to_split=0.16842722147088796
[LightGBM] [Warning] lambda_l1 is set=6.590011235194716e-07, reg_alpha=0.0 will be ignored. Current value: lambda_l1=6.590011235194716e-07
[LightGBM] [Warning] bagging_fraction is set=0.5649193881943484, subsample=1.0 will be ignored. Current value: bagging_fraction=0.5649193881943484
[LightGBM] [Warning] bagging_freq is set=2, subsample_freq=0 will be ignored. Current value: bagging_freq=2

============================================================
  INFERENCE RESULTS
============================================================

	Warehouse_block	Mode_of_Shipment	Weight_in_gms	on_time_probability	status	ground_truth
1666	C	Ship	2083	0.9936	On Time	1
6542	A	Ship	1823	0.5791	On Time	1
6428	A	Road	5839	0.4485	Delayed	0
8060	A	Road	4287	0.3439	Delayed	0
9822	D	Ship	5282	0.3525	Delayed	0
4478	A	Ship	4291	0.2371	Delayed	1

Inference complete: two artefacts, zero leakage, full reproducibility.

---

What You Built¶

You Wrote	BitBullet Handled
Feature descriptions in generate_feature_stats	Full statistical audit across all columns
Declared transforms in pipeline.add()	Fit, chain, state management, serialisation
Called pipeline.fit_transform(X_train)	Learned parameters on training data only
Called pipeline.save() / TransformPipeline.load()	Binary serialisation with exact parameter reload
Declared a TrainConfig	Optuna TPE search, Stratified K-Fold CV, early stopping
Called trainer.fit()	Optimisation, final training, threshold tuning, feature importance, SHAP
Changed model_type string	Full multi-architecture benchmark with zero boilerplate
Called ModelEvaluator.evaluate(...) and evaluate_classification(...)	Human diagnostics plus JSON-ready metric payloads
Populated ModelMetadata	JSON-exportable provenance with CV metrics, test metrics, schema, preprocessing, search, and inference-support metadata
Called ModelSerializer.save()	Self-describing package with datasets and metadata
Called TrainingReportGenerator.from_trainer_state()	Structured report + hyperparameter importance

Continue the Academy:

• 02_Multi_Class_Classification.ipynb — extend this workflow to a 4-class target
• 03_Clustering.ipynb — unsupervised segmentation with bitbullet.cluster
• 04_Data_Transformations.ipynb — deep dive into every transform in the library
• 05_Model_Management.ipynb — versioning, comparison, and model governance