SHAP

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

Defines feature-credit attribution and demonstrates native extraction, prediction reconstruction, summary plots, and JSON explanations without deriving Shapley mathematics.

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

A SHAP value is a feature-attribution value based on Shapley values that quantifies a feature's contribution to an individual prediction.

SHAP (SHapley Additive exPlanations) explains how a model arrives at individual predictions by assigning each feature a contribution value. Based on Shapley values from cooperative game theory, it distributes feature credit or importance across a prediction. For tabular models, these explanations can reveal how features are being used, challenge earlier assumptions, and identify features that are unhelpful or detrimental. section 7.3

Use SHAP after training to make a complex model easier to discuss with domain experts and business stakeholders. Explanations can support trust and transparency needs in regulated settings, and can help inspect possible bias in model decision-making. They complement exploratory data analysis: EDA suggests how data may be useful, while SHAP shows how the fitted model used it. section 7.3

For XGBoost or LightGBM models, obtain and inspect SHAP values through a practical workflow:

Generate contribution values directly from a trained booster with prediction contributions enabled; XGBoost and LightGBM can produce them natively. section 7.3 section 7.4
Check that the SHAP values reconstruct predictions in the model’s output representation; the illustrated regression output treats them as prediction multipliers. section 7.3
Use a bar importance plot to rank influential features, or a violin plot to inspect the distribution of contributions across observations. section 7.3 section 7.4
Package a selected prediction, its original feature values, and its SHAP values into JSON when an explanation must be passed into a prompt or another interface. section 7.3

Explaining a tabular regression prediction

Transform the input features with the fitted preprocessing pipeline, create an XGBoost DMatrix, and call the booster with pred_contribs=True. The resulting contribution array can be paired with transformed feature names, summarized in plots, or serialized with one prediction’s original values and SHAP values for a narrative explanation. 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

This listing shows the minimal XGBoost path: retrieve the booster, transform features, create a DMatrix, then generate both SHAP contributions and predictions. section 7.3

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

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Also explained in

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Ensemble Methods for Machine LearningSHAP is a model-agnostic explainability technique that attributes an individual model prediction to its input features using Shapley values.
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Machine Learning System DesignA feature-attribution technique based on Shapley values that assigns contributions of input features to a model prediction.
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Outlier Detection in PythonSHAP is an open-source Python library for explaining machine-learning model outputs using feature attribution methods based on Shapley values.
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Financial AI in PracticeA feature-attribution method that explains a model prediction by assigning each input feature a contribution based on Shapley values.

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Related concepts

interpretabilityused by · Interpretability is the degree to which people can understand a model’s behavior, outputs, predictions, or the factors that produce them.
Shapley valueuses · A Shapley value is a cooperative-game-theoretic attribution that assigns a participant its average marginal contribution across all possible coalitions.
model-agnostic methodkind of · A model-agnostic method analyzes predictions or estimates explanations without relying on a model’s internal structure, implementation, or architecture.
local explanationused by · A local explanation describes how or why a model produced a particular prediction or decision for an individual input.
feature attributionkind of · Feature attribution is a technique for estimating or assigning the contribution of individual input features to a model’s output.

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.