Computes Shapley-like additive feature attributions for individual
predictions. Uses kernel SHAP approximation for efficiency.
Usage
shap_values(explainer, new_data, n_samples = 100)
Arguments
- explainer
An OmicExplainer object
- new_data
Data for which to compute explanations
- n_samples
Number of samples for approximation (default: 100)
Value
A matrix of SHAP values (instances x features)
Details
SHAP (SHapley Additive exPlanations) values explain individual predictions
as the sum of feature contributions. For each prediction:
prediction = base_value + sum(SHAP_values)
Positive SHAP value = feature pushes prediction higher
Negative SHAP value = feature pushes prediction lower