How deeply does Ensemble Methods for Machine Learning cover model-agnostic method?

Defines the black-box distinction and works through LIME’s local surrogate, weighted loss, and fidelity-interpretability tradeoff.

Model-agnostic method

As explained in Ensemble Methods for Machine Learning

A model-agnostic explainability method explains a trained model or its predictions without relying on the model's internal architecture.

A model-agnostic method explains a machine-learning black box using its inputs and outputs rather than its internal architecture. It can therefore support global explanations of overall behavior or local explanations of individual predictions. section 9.3 section 9.0

Model-agnostic explainability includes:

Global methods such as permutation feature importance, partial dependence plots, and global surrogate models describe behavior across many examples. section 9.0
Local methods such as LIME and SHAP explain individual predictions so users can decide what to do next. section 9.0 section 9.3

LIME makes the idea concrete. For one example, it creates nearby surrogate examples, weights them by distance, and fits an interpretable linear model that approximates the black box locally. The method balances fidelity to the black box against interpretability, including the number of features used by the surrogate. section 9.4.1

Explaining a subscription prediction

A LIME explanation can show why an XGBoost prediction for a marketing test example was positive or negative: features supporting subscription appear on one side, while features opposing it appear on the other.

How Ensemble Methods for Machine Learning covers it

Cover of Ensemble Methods for Machine Learning

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Related concepts

LIMEincludes · LIME is a model-agnostic explanation technique that approximates a model near an individual input with an interpretable surrogate to explain its prediction.
permutation feature importanceincludes · Permutation feature importance measures a feature’s importance to a model by randomly permuting its values and assessing the resulting change in predictive performance.
SHAPincludes · SHAP is a feature-attribution method that explains model outputs by assigning input features contributions based on Shapley values.
importance scoreuses · An importance score is a numerical value indicating an item's relative significance, relevance, or contribution within a specified context.
surrogate modeluses · A surrogate model is an approximate model that estimates the behavior or outputs of a more complex, costly, or otherwise unavailable model, process, or function.

This explainer was generated by Manning's knowledge graph from the text of Ensemble Methods for Machine Learning. The section links point to the parts of the book it draws on.