sklearn-explainability

What problem does it solve?

This Skill helps users understand the decision-making process of machine learning models, ensuring scientific validity and identifying potential biases or artifacts.

Core Features & Use Cases

• Model Interpretability: Provides tools for both global and local explanations of model predictions.
• Feature Importance: Ranks the impact of features on model outcomes.
• Diagnostic Tools: Helps in validating model behavior against scientific principles.
• Use Case: In drug discovery, use this skill to verify that a model predicting compound efficacy relies on chemically meaningful features rather than spurious correlations.

Quick Start

Use the sklearn-explainability skill to analyze feature importance for the trained model on the test dataset.