Shapley value
A Shapley value is a cooperative-game-theoretic attribution that assigns a participant its average marginal contribution across all possible coalitions.
Ensemble Methods for Machine Learning
A Shapley value is a cooperative-game-theoretic attribution that assigns each player its average marginal contribution across possible coalitions.
- How it is used
- Attributes an individual prediction’s feature importance for local explanation of black-box models.
- How deep it goes
- Works through coalition values and weighted marginal differences, then defers scalable computation to SHAP and TreeSHAP.
- Examples
- Uses a four-person prize allocation and waterfall and force plots for an XGBoost prediction.
- What you can do after
- Apply it via SHAP or TreeSHAP to attribute feature contributions for individual predictions.
Read how Ensemble Methods for Machine Learning explains Shapley value →
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.
- How it is used
- Explains how individual machine-learning features contribute to specific predictions and supports model interpretability.
- How deep it goes
- Defines coalition-based contribution, additive attribution, and resampling approximation without deriving the underlying mathematics.
- Examples
- Uses XGBoost predictions, SHAP importance and violin plots, and JSON records pairing transformed features with contributions.
- What you can do after
- Diagnose model behavior with SHAP and TreeSHAP outputs, visualizations, and per-instance feature-contribution explanations.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains Shapley value →
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.
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