Scikit-learn

Scikit-learn is the foundational Python library for classical machine learning, providing robust implementations of regression, classification, clustering, dimensionality reduction, and pipelines.

When to Use

• Classical Machine Learning Algorithms: Linear models, decision trees, random forests, clustering, and dimensionality reduction.
• End-to-End ML Pipelines: Constructing robust pipelines combining imputers, encoders, scalers, and estimators.
• Cross-Validation & Hyperparameter Tuning: Stratified K-fold validation, GridSearchCV, and RandomizedSearchCV.
• Model Evaluation & Diagnostic Metrics: Confusion matrices, ROC-AUC curves, precision-recall tradeoffs, and classification reports.

Quick Start

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer