scikit-learn

You can build a machine learning pipeline for tabular data by integrating preprocessing, feature engineering, and cross-validation within reusable pipelines. This approach trains, evaluates, and compares multiple models to select the best one for deployment.

Comparing regression and classification models involves training multiple supervised learning algorithms and evaluating them with specific metrics. You can structure this within pipelines to systematically assess performance and select the best model for your structured data.

Yes, you can perform unsupervised learning and clustering on data without labels to discover underlying structure. The Skill provides algorithms designed to find patterns in unannotated datasets, helping you extract insights from structured data.

Yes, the workflow supports hyperparameter tuning and cross-validation to ensure reproducible model evaluation. These steps are integrated directly within the pipelines, allowing you to optimize and validate supervised and unsupervised learning models effectively.

Classical ML workflows are limited to tabular data and do not natively handle unstructured inputs like raw images or text deep learning requires. They focus on accessible, production-ready modeling for structured datasets rather than complex neural network architectures.