select-features

What problem does it solve?

Select-features reduces dimensionality and prevents overfitting by identifying the most predictive engineered features, improving model generalization and reducing training time.

Core Features & Use Cases

• Feature group analysis: evaluates logical feature groups (core, temporal, domain-specific) to identify signal vs. noise.
• Forward selection with monitoring: greedily adds features while tracking CV accuracy and overfit gap to avoid overfitting.
• Notebook and report generation: outputs a feature selection notebook and a structured summary documenting results and recommendations.

Quick Start

Run the feature-selection steps on your engineered feature set to identify the optimal subset for modeling.