Can you use SHAP values for feature selection?
mediumAnswer
- Yes — mean absolute SHAP value across a validation sample gives a robust, model-consistent importance ranking.
- Advantages over impurity: unbiased for cardinality, respects the model's actual behavior, works per-example.
- Practical selection: sort by |, keep features that together explain a target fraction (e.g., 95%) of total absolute SHAP, or drop features below a floor threshold.
- Downside: computing SHAP is more expensive than impurity, and highly correlated features share importance in ways that can be misleading.
Check yourself — multiple choice
- SHAP can only be used for classification
- Rank by validation; drop features below a threshold or below cumulative-share cutoff
- SHAP is unrelated to feature importance
- SHAP requires linear models
| = robust importance; select by threshold or cumulative-share cutoff.
#feature-selection#interpretability
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