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TreeSHAP

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

TreeSHAP is an algorithm for efficiently computing SHAP values for tree-based models.

TreeSHAP efficiently computes SHAP values for tree-based models, showing how each feature contributes to an individual prediction. This supports model debugging, feature engineering, interpretability, bias investigation, and conversations with stakeholders or regulators. section 7.3

SHAP values distribute a prediction’s feature credit using Shapley values from cooperative game theory. Rather than evaluating every feature combination, SHAP approximates contributions through resampling; tree implementations make this practical for models such as XGBoost and LightGBM. section 7.3

A practical TreeSHAP workflow includes:

  • Retrieve the trained tree model and transform input data through the existing preprocessing pipeline. section 7.3
  • Generate per-feature contributions, compare them with predictions, and inspect global importance or violin plots. section 7.3
  • Package feature values and SHAP values as JSON to create readable explanations for individual predictions. section 7.3
Listing 7.19 SHAP values as an XGBoost output
from XGBoost import DMatrix
 
booster = model_pipeline['xgb'].get_booster()              #1
 
Xt = model_pipeline['processing'].transform(X)             #2
feature_names = (
    model_pipeline['processing']
    .get_feature_names_out()
)                                                          #3
Xd = DMatrix(Xt)                                           #4
 
shap_values = booster.predict(Xd, pred_contribs=True)      #5
preds = booster.predict(Xd)                                #6

Inspect the XGBoost implementation that extracts transformed features, constructs a DMatrix, and requests prediction contributions. section 7.3

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

How deep it goes
Defines SHAP and demonstrates extraction, visualization, and JSON packaging, but stops short of mathematical derivation or algorithmic internals.
Examples
Uses XGBoost contribution output, importance and violin plots, and JSON explanations containing feature values and SHAP contributions.
What it assumes
Assumes a trained tree-based model and transformed input data, while defining SHAP values and Shapley values from first principles.
What you can do after
Apply it via SHAP or native XGBoost and LightGBM procedures to inspect and communicate tree-model predictions.
How it connects
Links TreeSHAP with SHAP values, Shapley values, XGBoost, LightGBM, model debugging, and interpretability.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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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.