SHAP for Machine Learning: Comparing Explainers in a Practical Guide

SHAP has become a standard in ML. It is a framework that shows how much each feature contributes to a model's prediction. But how do you choose among the different interpretation methods? A new guide offers a practical answer.

Four ways to explain a model

The GitHub guide compares different SHAP explainers on the same data, using decision trees and other models. It turns out that the way you calculate feature importance affects both the result and the speed.

• Tree explainer works only with tree models and is the fastest
• Exact explainer is mathematically exact but slow on large models
• Permutation explainer is universal and works with any model
• Kernel explainer is the most flexible but needs a lot of memory

When to use which method

If your model is a decision tree or a random forest, the Tree explainer finishes the job in milliseconds. If you need absolute precision on small data, the Exact explainer will not let you down. For black boxes (neural networks, XGBoost), Permutation or Kernel fits: the first is faster, the second more precise.

The guide also shows how to track drift, when a model degrades over time. SHAP helps identify which features have started behaving strangely. For interactions between features (when A and B together matter more than separately), there are dedicated methods.

Practice vs. theory

On real data, the Tree explainer is 100+ times faster than Exact, but Exact always gives the same result, while Tree can vary depending on the structure. Permutation works with anything but requires heavy computation on large datasets. Kernel is the slowest, but it handles local explanations around a point of interest best.

What this means

Explainability of an ML model is not a luxury but a necessity. Regulators require explanations of why a model rejected a loan or diagnosed a disease. SHAP is one of the tools that does this. The new guide shows there is no universal solution: choose an explainer based on the model type, the data volume and the required accuracy.