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TreeExplainer

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

TreeExplainer is a SHAP explainer for computing feature attributions from tree-based machine-learning models.

TreeExplainer attributes predictions from tree-based models to their input features, helping explain why an individual prediction or a broader model pattern occurs. section 7.3

Choose the calculation approach according to the explanation goal:

  • Use tree-path-dependent perturbation when reconstructing the model's original predictions is important. section 7.3
  • Use interventional perturbation when correlated features make more causally informed contribution estimates valuable, accepting longer computation and weaker prediction reconstruction. section 7.3
  • Inspect global importance with bar plots or feature effects with violin plots, then package individual explanations as JSON containing values and SHAP contributions. section 7.3

Explain an XGBoost prediction

Transform the input data, construct a TreeExplainer for the XGBoost model, request raw interventional SHAP values, and compare their interpretation with the model's prediction. section 7.3

from shap import TreeExplainer

explainer = TreeExplainer(model_pipeline['xgb'], data=Xt, model_output='raw', feature_perturbation='interventional')
interventional_shap_values = explainer.shap_values(Xt)

Open the runnable interventional TreeExplainer setup. section 7.3

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Shows configuration trade-offs and practical outputs, but defers mathematical derivation and detailed implementation internals.
Examples
Uses XGBoost predictions, importance and violin plots, and JSON explanations pairing transformed feature values with SHAP contributions.
What it assumes
Assumes a trained XGBoost pipeline, transformed feature data, and familiarity with SHAP values and model predictions.
What you can do after
Apply TreeExplainer to generate and package feature attributions while selecting a perturbation method for the explanation goal.
How it connects
Connects SHAP values with XGBoost, feature perturbation, collinearity, prediction reconstruction, and feature-importance plots.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.