Scikit-learn Debugger

소개

This skill provides a specialized framework for troubleshooting machine learning workflows built with Scikit-learn. It offers precise guidance for resolving frequent bottlenecks such as shape mismatches between training and testing sets, handling missing or infinite values, and fixing complex Pipeline or ColumnTransformer configurations. Beyond error resolution, it assists in detecting data leakage, analyzing learning curves to balance bias and variance, and addressing optimizer convergence warnings to ensure your models are robust and production-ready.

주요 기능

• Data integrity checks for NaN values, infinity, and feature consistency between datasets
• Optimization strategies for convergence warnings and cross-validation failures
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• Pipeline structure inspection to verify transformer-predictor compatibility
• Automated diagnosis for common exceptions like NotFittedError and shape alignment ValueErrors
• Data leakage detection tools for identifying suspiciously high performance metrics

사용 사례

• Resolving feature mismatches between training and inference data in complex pipelines
• Fixing convergence warnings in linear models by adjusting solvers and feature scaling
• Diagnosing and correcting data leakage that causes over-optimistic model evaluation