scikit-learn

Machine learning pipelines prevent data leakage by bundling preprocessing and modeling steps together. Using scikit-learn's ColumnTransformer and Pipeline ensures transformations are fitted only on training data during cross-validation, keeping evaluation rigorous and reproducible.

Cross-validation and model selection for classification are configured using scikit-learn's GridSearchCV. It systematically searches hyperparameter combinations, evaluates performance across data folds, and identifies the optimal model configuration while mitigating overfitting.

Handling mixed data types in scikit-learn preprocessing is best done with ColumnTransformer. It applies specific transformations like scaling or encoding to distinct feature columns simultaneously, seamlessly integrating numerical and categorical data into a unified pipeline.

Yes, you can use scikit-learn for both classification and clustering on the same dataset. The framework provides algorithms for supervised classification and unsupervised clustering, allowing you to apply predictive modeling and exploratory grouping within the same workflow.

Cross-validation scores drop significantly compared to training accuracy due to overfitting. When preprocessing steps are applied outside a unified pipeline, test data leaks into training. Bundling steps via scikit-learn pipelines ensures robust, generalizable model evaluation.