Version Compatibility

Reference examples tested with: numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, shap 0.44+, lime 0.2+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

Two shap drifts: the Explanation-object plotting API arrived ~0.36 (new-style shap.plots.* take an Explanation, legacy shap.summary_plot/dependence_plot take numpy arrays -- mixing them is the most common runtime error); and TreeExplainer(..., feature_perturbation='auto') became the default in 0.47 (was interventional), so providing or omitting data= silently changes the estimand. Always set feature_perturbation explicitly. If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt rather than retrying.

Model Interpretation for Omics Classifiers

"Which genes drive my classifier?" -> Compute attributions, but treat them as a description of the model (not biology), choose the Shapley conditioning deliberately, and aggregate over correlated gene modules before ranking.

• Tree models: shap.TreeExplainer(model, data=background, feature_perturbation='interventional')
• Model-agnostic local: lime.lime_tabular.LimeTabularExplainer
• Model-reliance screen: sklearn.inspection.permutation_importance

The Single Most Important Modern Insight -- Attributions Explain the Model, Not Biology, and Under Correlation the Algorithm Chooses How to Split Credit

A feature attribution describes the function the model learned on this training distribution; it is not a measurement of biology. A high-SHAP gene can be a pure correlate of a batch, scanner, or library-prep signal the model exploited (DeGrave 2021 is the canonical proof). And because genes co-express in tight modules, the attribution algorithm has genuine freedom in how it splits credit among correlated genes -- the choice of conditioning (tree_path_dependent/conditional vs interventional/marginal) is not a cosmetic knob, it changes which genes get credit, and it is a live methodological controversy with no universally correct answer (Janzing 2020). The operational rule: SHAP/LIME rankings are a debugging and hypothesis-generation tool, never a validated biomarker-selection criterion, and within a co-expression module the ordering is not a finding.