bio-machine-learning-prediction-explanation

What problem does it solve?

Interpreting machine learning predictions on omics data is challenging; this skill provides transparent explanations by computing SHAP values and LIME attributions to reveal which features drive a classifier's decisions.

Core Features & Use Cases

• Global SHAP-based feature importance visualization for global interpretation
• Local explanations for individual samples using LIME
• Biomarker discovery and interpretation across omics datasets
• Supports both tree-based and linear models and multi-class predictions

Quick Start

Provide a trained omics classifier and a dataset; request SHAP and LIME explanations to interpret model predictions.