alterlab-scikit-learn

What problem does it solve?

Provides comprehensive guidance for constructing, evaluating, and deploying classical machine learning workflows using scikit-learn, enabling fast, repeatable experiments for researchers and engineers.

Core Features & Use Cases

• Classification & Regression: Build, evaluate, and compare models with common algorithms (Logistic Regression, Random Forest, Gradient Boosting, SVM).
• Unsupervised & Dimensionality Reduction: Apply clustering, PCA, t-SNE, and related methods to explore data structure.
• Pipelines & Preprocessing: Create end-to-end pipelines with preprocessing, cross-validation, and hyperparameter tuning.

Quick Start

Train and evaluate a complete classification pipeline from data loading to prediction using a simple dataset.