ML Feature Engineer

关于

The ML Feature Engineer skill provides a systematic framework for transforming raw datasets into high-performance features. It streamlines the entire machine learning lifecycle by automating data quality reporting, feature creation (temporal, behavioral, and interaction), dimensionality reduction via advanced selection methods, and robust transformations like scaling and encoding. Built to integrate with the SpecWeave development workflow, it ensures that every engineering step is documented, validated for data leakage or drift, and versioned for production reliability.

主要功能

• Comprehensive validation for data leakage and distribution drift
• Production-ready scaling, encoding, and power transformations
• 13 GitHub stars
• Automated data quality assessment and outlier detection
• Multi-method feature selection using statistical tests and model importance
• Domain-specific feature creation including temporal and aggregation logic

使用场景

• Building predictive models from raw transactional or behavioral data logs
• Standardizing data preprocessing for production-grade AI applications
• Optimizing ML pipeline performance by reducing feature redundancy and noise