scikit-learn - Machine Learning in Python

A robust library for classical machine learning. It features a uniform API: all objects share the same interface for fitting, transforming, and predicting.

When to Use

• Classification: Detecting categories (Spam vs. Ham, Disease diagnosis).
• Regression: Predicting continuous values (House prices, Stock trends).
• Clustering: Grouping similar objects (Market segmentation, Image compression).
• Dimensionality Reduction: Reducing feature count while keeping info (PCA, Visualization).
• Model Selection: Comparing models and tuning hyperparameters (Cross-validation, Grid search).
• Preprocessing: Transforming raw data into features (Scaling, Encoding, Imputation).

Reference Documentation

Official docs: https://scikit-learn.org/stable/
User Guide: https://scikit-learn.org/stable/user_guide.html
Search patterns: sklearn.pipeline.Pipeline, sklearn.model_selection, sklearn.ensemble, sklearn.preprocessing