Global explanation
A global explanation describes a model’s overall behavior or decision patterns across a range of inputs rather than explaining a single prediction.
Ensemble Methods for Machine Learning
A global interpretability method explains a model's overall behavior, features, and decision-making patterns across a broad range of examples.
- How it is used
- Explains overall model behavior, broadly relevant factors, and deployment-time patterns across many examples.
- How deep it goes
- Covers model-agnostic methods, feature scores, marginalization, surrogate fidelity, and glass-box EBM structure.
- Examples
- Uses feature-importance scores, dependency plots, Bank Marketing predictions, and an ExplainableBoostingClassifier.
- What you can do after
- Evaluate model-wide behavior with importance scores and dependency plots, or train an interpretable EBM with InterpretML.
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Outlier Detection in Python
A global explanation describes how an outlier detector behaves across the dataset, including the patterns it identifies and how it assigns scores.
- How it is used
- Explains detector-wide scoring patterns for assessment, debugging, and comparing detectors when forming ensembles.
- How deep it goes
- Describes detector-specific summaries and feature associations, while noting that useful global explanations remain subjective and local explanations are difficult.
- Examples
- Uses two-dimensional KNN ensembles on abalone data and aggregates influential feature pairs, including repeated involvement of Height.
- What you can do after
- Diagnose/debug detectors by locating emphasized or missed anomaly patterns and compare which detectors identify relevant outlier types.
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Related concepts
- local explanationcontrasts with · A local explanation describes how or why a model produced a particular prediction or decision for an individual input.
- importance scoreincludes · An importance score is a numerical value indicating an item's relative significance, relevance, or contribution within a specified context.
- ensembleuses · An ensemble is a technique that combines or considers outputs from multiple models or component methods collectively to produce or assess a joint result.
- explainable outputkind of · An explainable output is a result accompanied by an intelligible account of the factors, reasoning, or decision logic underlying it.
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