LIME
LIME is a model-agnostic explanation technique that approximates a model near an individual input with an interpretable surrogate to explain its prediction.
Ensemble Methods for Machine Learning
LIME is a model-agnostic explanation technique that approximates individual predictions with an interpretable model fitted near the input being explained.
- How it is used
- Explains individual predictions from ensembles and other black-box machine-learning models.
- How deep it goes
- Defines perturbation, distance weighting, weighted loss, and linear surrogates, but stops short of explaining feature interactions.
- Examples
- Uses data-driven marketing, an XGBoost subscription model, categorical inputs, and a true-positive test prediction.
- What you can do after
- Apply it via lime_tabular to inspect feature contributions for a selected prediction.
Read how Ensemble Methods for Machine Learning explains LIME →
Machine Learning System Design
LIME is a model-agnostic explanation technique that approximates a complex model locally around a particular input with a simpler interpretable model.
- How it is used
- Explains individual predictions and supports feature-selection decisions in model development.
- How deep it goes
- Defines local approximation and practical comparison, but stops short of mathematical derivation or implementation detail.
- Examples
- Uses retail demand features such as promotions, discounts, traffic, SKU history, and feature-quality tests.
- What you can do after
- Evaluate feature contributions for individual predictions and inform feature retention or removal.
Financial AI in Practice
A model-agnostic explanation method that approximates a model locally with an interpretable model around a prediction.
- How it is used
- Explains individual credit decisions for borrower conversations and compliance.
- How deep it goes
- Defines a quick local approximation, without detailing its implementation.
- Examples
- Uses a single credit applicant’s profile as the explanation scenario.
- What you can do after
- Understand how a local model can clarify a specific credit prediction.
Related concepts
- 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 surrogate methodkind of · A local surrogate method explains a black-box model near a particular input by fitting an interpretable approximation to its local behavior.
- interpretabilityused by · Interpretability is the degree to which people can understand a model’s behavior, outputs, predictions, or the factors that produce them.
- local explanationused by · A local explanation describes how or why a model produced a particular prediction or decision for an individual input.
- explainability frameworkused by · An explainability framework is a collection of methods, controls, or tools that helps people understand a model’s outputs or decision process.
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