Shapley value

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover Shapley value?

Defines coalition-based contribution, additive attribution, and resampling approximation without deriving the underlying mathematics.

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

A Shapley value is a cooperative-game-theory allocation that fairly assigns each participant a contribution across all possible coalitions.

A Shapley value fairly assigns each participant’s contribution across every possible coalition. For machine learning, SHAP values adapt this idea to assign each feature credit for a specific prediction. section 7.3

The practical interpretation follows three ideas:

Each feature’s contribution reflects its influence across different feature combinations. section 7.3
SHAP uses resampling to approximate the otherwise expensive calculation over all combinations. section 7.3
The resulting explanations support model debugging, feature engineering, bias analysis, and interpretability. section 7.3

Explaining an XGBoost prediction

An XGBoost model can produce SHAP values for each transformed feature, compare them with predictions, and present global importance through bar or violin plots. A JSON explanation can pair each feature’s original value with its SHAP contribution for narrative interpretation. 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 runnable XGBoost procedure for obtaining transformed features, feature names, predictions, and SHAP contributions. section 7.3

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Ensemble Methods for Machine LearningA Shapley value is a cooperative-game-theoretic attribution that assigns each player its average marginal contribution across possible coalitions.

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Related concepts

SHAPused by · SHAP is a feature-attribution method that explains model outputs by assigning input features contributions based on Shapley values.
nixtlaused by · nixtla is a Python package that provides programmatic access to Nixtla's time-series forecasting services and models.
importance scoremeasures · An importance score is a numerical value indicating an item's relative significance, relevance, or contribution within a specified context.
ensembleused by · An ensemble is a technique that combines or considers outputs from multiple models or component methods collectively to produce or assess a joint result.
generationproduces · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

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.