Train GradientBoostingClassifier with 5-Fold Cross-Validation

Q: Train GradientBoostingClassifier with 5-Fold Cross-Validation

This question evaluates proficiency in supervised machine learning model development and evaluation, including use of scikit-learn pipelines, GradientBoostingClassifier configuration, stratified 5-fold cross-validation, ROC-AUC performance measurement, and model serialization.

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Question

Final Model Training: GradientBoostingClassifier with 5-Fold CV

Context

Assume the notebook already contains a prepared feature matrix X and a binary target y (0/1), with any necessary preprocessing completed. Your goal is to evaluate and train a final model.

Task

Build a scikit-learn Pipeline that uses a GradientBoostingClassifier.
Evaluate the model using 5-fold Stratified cross-validation and report the mean ROC-AUC.
Fit the model on the full dataset (X, y).
Save the trained pipeline to disk as model.pkl.

Deliverable

Provide the full Python code that performs all steps above.

Train GradientBoostingClassifier with 5-Fold Cross-Validation

Final Model Training: GradientBoostingClassifier with 5-Fold CV

Context

Task

Deliverable

Solution

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Train GradientBoostingClassifier with 5-Fold Cross-Validation

Overview

Final Model Training: GradientBoostingClassifier with 5-Fold CV

Context

Task

Deliverable

Solution

Comments (0)