SHAP for machine learning: comparing explainers and a practical guide
SHAP is a tool for explaining ML models. A new guide compares four interpretation methods: Tree for tree-based models, Exact for precision, Permutation for flex
SHAP has become the standard in ML — it's a framework that shows how much each feature contributes to a model's prediction. But how do you choose between different interpretation methods? A new guide provides a practical answer.
Four Ways to Explain a Model
A GitHub guide compares different SHAP explainers on the same data — decision trees and other models. It turns out that the way feature importance is calculated affects both the result and the speed.
- Tree explainer — works only with trees, the fastest
- Exact explainer — mathematically precise, but slow on large models
- Permutation explainer — universal, works with any model
- Kernel explainer — most flexible, but requires a lot of memory
When to Use Which Method
If the model is a decision tree or random forest, Tree explainer will do the job in milliseconds. If absolute accuracy on small data is needed, Exact explainer won't disappoint. For black boxes (neural networks, XGBoost), Permutation or Kernel are suitable — the first is faster, the second is more accurate.
The guide also shows how to track drift — when a model degrades over time. SHAP helps understand which features have started behaving strangely. For interactions between features (when A and B together are more important than separately) there are separate methods.
Practice vs Theory
On real data, Tree explainer is 100+ times faster than Exact, but Exact always gives one result, while Tree can vary depending on structure. Permutation works with anything, but requires heavy computation on large datasets. Kernel is the slowest of all, but best understands local explanations around the point of interest.
What This Means
ML model interpretability is not a luxury, but a necessity. Regulators require explaining why a model rejected a credit application or diagnosed a disease. SHAP is one of the tools that does this. The new guide shows that there is no universal solution: choose an explainer depending on the type of model, data volume, and required accuracy.