Machine Learning Feature Engineering

소개

The Feature Engineering Toolkit empowers Claude to systematically improve machine learning model accuracy and efficiency by automating the most time-consuming aspects of data preparation. By analyzing datasets to generate interaction terms, performing automated feature selection via importance scores, and handling essential transformations like scaling and encoding, this skill streamlines the entire data science workflow. It ensures that models are built on high-quality, relevant signals while reducing computational complexity and preventing common pitfalls such as feature dominance or incorrect categorical handling.

주요 기능

• Automated data validation to ensure clean input for ML pipelines
• Automated feature scaling and normalization for numerical datasets
• Dynamic creation of interaction terms and derived features
• Intelligent categorical encoding including one-hot and label methods
• Feature importance analysis using Random Forest and SelectKBest
• 3 GitHub stars

사용 사례

• Transforming raw data into model-ready formats using standardized scaling and encoding
• Reducing model complexity by selecting the top relevant features from large datasets
• Improving model accuracy by generating interaction terms between key variables