feature-engineer

What problem does it solve?

This Skill streamlines the creation of machine learning features, automating complex tasks like encoding, scaling, and selection to improve model performance.

Core Features & Use Cases

• Feature Transformation: Apply various encoding (one-hot, label) and scaling (min-max, standard) techniques.
• Interaction Terms & Embeddings: Generate polynomial features or word embeddings for richer data representation.
• Feature Selection: Implement methods like RFE or SelectKBest to identify the most impactful features.
• Use Case: You have a dataset with categorical and numerical features. Use this Skill to one-hot encode categorical variables, scale numerical ones, and select the top 20 features for your classification model.

Quick Start

Use the feature-engineer skill to create interaction terms for columns 'A' and 'B' in the provided dataset.