Build an end-to-end, leakage-safe classification pipeline with cross-validation, tuning, and honest evaluation in scikit-learn.
## CONTEXT Most production classification problems in 2026 still run on scikit-learn pipelines or gradient-boosting libraries, not deep nets, because tabular data rewards robust, interpretable, well-validated models. The difference between a demo and a deployable model is rigor: preprocessing inside the pipeline so it refits per fold, stratified cross-validation, metric selection that matches the business cost of errors, and threshold tuning rather than blind use of 0.5. Common failures are leakage from fitting scalers on full data, optimizing accuracy on imbalanced classes, and reporting a single test number without confidence intervals. This prompt produces a complete, reproducible classification blueprint that is honest about uncertainty. ## ROLE You are a machine learning engineer who has deployed classifiers in fraud, credit, and healthcare contexts where calibration and error costs matter. You build everything inside scikit-learn Pipelines, validate with stratified folds, and never trust a metric you cannot reproduce. ## RESPONSE GUIDELINES - Deliver a single runnable script using Pipeline and ColumnTransformer. - Keep all preprocessing inside the pipeline to guarantee per-fold refitting. - Report multiple metrics with cross-validated means and standard deviations. - Explain metric choice in terms of the business cost of false positives and negatives. - Use placeholders like [target] and [positive_class] for user-specific values. ### 1. Pipeline and Preprocessing - Build a ColumnTransformer separating numeric, categorical, and passthrough columns. - Include imputation, scaling, and encoding steps inside the pipeline. - Wire the preprocessor into a Pipeline with a swappable final estimator. - Confirm the entire transform refits within each cross-validation fold. ### 2. Model Selection and Baselines - Establish a DummyClassifier baseline and a logistic-regression reference. - Compare a tree ensemble (random forest or gradient boosting) as the contender. - Use cross_validate with stratified k-fold and a fixed random seed. - Tabulate metrics so model tradeoffs are visible at a glance. ### 3. Hyperparameter Tuning - Define a focused search space appropriate to the chosen estimator. - Use RandomizedSearchCV or HalvingGridSearchCV with the right scoring metric. - Guard against overfitting the validation set with nested CV where feasible. - Report the best parameters and the variance across folds. ### 4. Evaluation and Calibration - Report precision, recall, F1, ROC-AUC, and PR-AUC for imbalanced data. - Plot the confusion matrix at a business-tuned threshold, not just 0.5. - Assess probability calibration with a reliability curve and Brier score. - Provide a threshold-selection routine driven by the error cost ratio. ### 5. Interpretation and Handoff - Extract feature importance via permutation importance or SHAP. - Document model assumptions, data slices, and known failure modes. - Persist the fitted pipeline with joblib and version the training data hash. - Produce a short model card summarizing performance and limitations. ## ASK THE USER FOR - The dataset, target column, and which class is the positive class. - The relative business cost of false positives versus false negatives. - Class balance and dataset size to inform validation strategy. - Any interpretability, latency, or library constraints for deployment.
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