scikit-learn

What problem does it solve?

Guidance and patterns for using scikit-learn to build, evaluate, and deploy classical ML models.

Core Features & Use Cases

• Supervised learning: classification and regression
• Unsupervised learning: clustering and dimensionality reduction
• Model evaluation, cross-validation, and hyperparameter tuning
• Data preprocessing and pipeline construction
• End-to-end examples for common ML tasks

Quick Start

Install scikit-learn and run a quick classification example:

1. pip install scikit-learn
2. from sklearn.model_selection import train_test_split; from sklearn.ensemble import RandomForestClassifier X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)