SHAP
SHAP is a feature-attribution method that explains model outputs by assigning input features contributions based on Shapley values.
Ensemble Methods for Machine Learning
SHAP is a model-agnostic explainability technique that attributes an individual model prediction to its input features using Shapley values.
- How it is used
- Explains individual black-box ensemble predictions through model-agnostic feature attributions.
- How deep it goes
- Derives feature credit from Shapley coalitions, then addresses practical approximation and efficient tree-based computation.
- Examples
- Uses a four-person Kaggle prize coalition and TreeSHAP explanations of an XGBoost test example with waterfall and force plots.
- What you can do after
- Apply it via TreeSHAP and visualization plots to interpret an individual XGBoost prediction.
Read how Ensemble Methods for Machine Learning explains SHAP →
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.
- How it is used
- Explains individual XGBoost predictions for stakeholder communication, transparency, bias inspection, and feature review.
- How deep it goes
- Defines feature-credit attribution and demonstrates native extraction, prediction reconstruction, summary plots, and JSON explanations without deriving Shapley mathematics.
- Examples
- Uses transformed tabular inputs, XGBoost prediction contributions, importance and violin plots, and a JSON prediction explanation prompt.
- What you can do after
- Apply it via XGBoost, LightGBM, or the shap package to inspect feature contributions and communicate predictions.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains SHAP →
Machine Learning System Design
A feature-attribution technique based on Shapley values that assigns contributions of input features to a model prediction.
- How it is used
- Explains individual model predictions and supports feature-importance analysis during feature engineering and selection.
- How deep it goes
- Defines contribution estimation by averaging across feature combinations, but does not derive the underlying Shapley mathematics.
- Examples
- Uses retail demand features, deep-learning pixels and tokens, and comparisons with LIME and shuffle importance.
- What you can do after
- Evaluate/understand which input features contribute to predictions and inform feature-removal decisions.
Outlier Detection in Python
SHAP is an open-source Python library for explaining machine-learning model outputs using feature attribution methods based on Shapley values.
- How it is used
- Explains outlier-detector scores by attributing them to features globally and for individual records.
- How deep it goes
- Demonstrates implementation and interpretation, but defers exact feature-score relationships and notes that SHAP can be slower.
- Examples
- Uses abalone records, Sex and Height, bar plots, beeswarm plots, and a highest-scored local explanation.
- What you can do after
- Diagnose outlier scores with SHAP global and local plots while checking whether feature combinations remain ambiguous.
Financial AI in Practice
A feature-attribution method that explains a model prediction by assigning each input feature a contribution based on Shapley values.
- How it is used
- Supports customer-service and compliance explanations of individual credit decisions.
- How deep it goes
- Defines feature contributions through a credit example but does not detail calculations or implementation.
- Examples
- Contrasts rising revenue’s positive contribution with revolving credit balance’s larger negative contribution.
- What you can do after
- Apply feature contributions to explain individual credit decisions and derive reason codes.
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.
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