machine-learning

You build a reproducible machine learning pipeline by enforcing strict train/validation/test separation, applying stratified cross-validation, and logging configurations with metrics to recreate results across PyTorch and scikit-learn workflows.

Leakage-safe hyperparameter tuning prevents test data from contaminating validation metrics during model selection. It enforces strict separation between training, validation, and test sets using stratified cross-validation so final performance metrics remain trustworthy.

You generate SHAP explanations by applying SHAP value calculations to the final best model after training and hyperparameter tuning. This produces feature importance insights that provide defensible reasoning for individual predictions.

Yes, you can use Optuna for hyperparameter search across PyTorch and scikit-learn workflows. It performs systematic hyperparameter optimization while maintaining leakage-safe experiment design through stratified cross-validation.

Yes, this approach supports experiment tracking for classification and regression projects by logging configurations, metrics, and artifacts during training and evaluation. This ensures your machine learning experiments can be fully recreated later.

Hyperparameter tuning produces misleading test metrics when validation data leaks into the training process. Applying leakage-safe experiment design with strict train/validation/test separation and stratified cross-validation prevents this contamination.