Interpretability — SHAP, LIME & Counterfactuals Training | Biostatistics & ML for Omics

Explain biomedical ML models using SHAP, LIME and counterfactuals. Learn global and local explanations, feature attributions, stability checks and how to communicate trustworthy insights for omics and clinical models.

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Interpretability — SHAP, LIME & Counterfactuals — Hands-on

Learn how to open the black box of biomedical and omics machine learning models. This module walks through global and local interpretability using SHAP and LIME, counterfactual explanations, stability checks, and practical reporting patterns so that domain teams can trust and act on model outputs.

Interpretability — SHAP, LIME & Counterfactuals

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Session Index

Session 1 — Foundations of Model Interpretability Session 2 — SHAP & LIME for Global & Local Explanations Session 3 — Counterfactuals, What Ifs & Sensitivity Session 4 — Trust, Communication & Audit Trails

Session 1

Fee: Rs 8800

Foundations of Model Interpretability

Why interpretability matters in biomedicine

regulatory and clinical expectations debugging and model trust safety and bias detection

Types of explanations and scopes

global vs local explanations model specific vs model agnostic post hoc vs intrinsic interpretability

Human factors in explanation design

audience awareness (clinician vs data scientist) cognitive load and visual choices limits of what explanations can claim

Session 2

Fee: Rs 11800

SHAP & LIME for Global & Local Explanations

SHAP concepts and workflows

Shapley value intuition tree based SHAP vs model agnostic global importance and dependence plots

LIME for local explanation of predictions

local surrogate models neighbourhood sampling choices strengths and caveats of LIME

Visualizing and comparing explanations

summary plots for feature impact per patient explanation dashboards alignment with domain knowledge

Session 3

Fee: Rs 14800

Counterfactuals, What Ifs & Sensitivity

Counterfactual explanation basics

what if style explanations plausibility and actionability constraints multiple valid counterfactuals

Sensitivity of explanations and robustness checks

stability across random seeds and folds perturbation tests on inputs detecting spurious shortcuts

Linking interpretability to fairness analysis

group wise explanations identifying systematically high risk subgroups inputs for bias mitigation workflows

Session 4

Fee: Rs 18800

Trust, Communication & Audit Trails

Communicating explanations to domain experts

storytelling with SHAP and LIME plots summary paragraphs in plain language flags and caveats in reports

Documentation and audit trails for explanations

storing explanation artefacts versioning models and explanation configs traceability for regulated use cases

Deliverables: interpretability report pack

global and local explanation figures R / Python scripts using SHAP and LIME template narrative for methods section