marcotcr/lime

Features

• Model Interpretability Frameworks - Provides a comprehensive framework for generating local interpretable explanations for predictions across diverse data types.
• Model Interpretability Tools - Uses local surrogate models to approximate complex model behavior and explain individual predictions.
• Interpretability Visualizations - Visualizes specific image segments that drive a machine learning model's classification decision.
• Feature Contribution Analysis - Identifies the most influential pixels in an image by perturbing inputs and observing the change in model predictions.
• Local Interpretability Tools - Generates local interpretable approximations to explain why a specific instance was assigned a particular class.
• Local Surrogate Models - Approximates complex model behavior locally using linear models to identify the most influential features.
• Regression Diagnostics - Generates local explanations for continuous value predictions to understand the drivers of regression outputs.
• Explanation - Identifies specific words or phrases within a document that positively or negatively influence a model's label.
• Text Attribution - Identifies specific words in a document that contribute positively or negatively to a model prediction.
• Local Surrogate Approximations - Uses local linear approximations to identify features influencing a single model prediction.
• Model Agnostic Interpretability - Implements an architecture-independent approach to interpret any classifier via a generic prediction interface.
• Model Explainability - Generates local interpretable explanations for individual predictions made by any machine learning classifier.
• Perturbation-Based Sampling - Creates local datasets by randomly altering features of a single instance to observe changes in model predictions.
• Text Classifiers - Identifies the influence of individual words on a classifier's prediction for single text instances.
• Applied Machine Learning - Provides a framework for identifying the most influential features for individual machine learning predictions.
• Classifier Interpretability - Visualizes the specific segments or pixels of an image that most strongly drive classification decisions.
• Tabular Explanations - Generates local explanations for tabular data by perturbing features and training a surrogate linear model.
• Tabular Model Explanation - Analyzes how numerical and categorical features in a dataset contribute to classifier or regression model predictions.
• Feature Binning - Converts numerical features into quartiles or bins to improve the human interpretability of model explanations.
• Feature Importance Attribution - Renders feature contributions as bar plots and highlighted image segments to visualize prediction drivers.
• Distribution-Based Sampling - Generates perturbed categorical data by sampling values according to their original training set distribution.
• Linear Regression - Fits a weighted linear regression model to perturbed samples to identify local feature influences.
• Linear Regression Models - Highlights the most influential features contributing to a continuous value prediction in regression models.
• Linear Model Interpretability - Provides local interpretable explanations for the output of regression models to clarify prediction drivers.
• Regression Approximations - Generates interpretable approximations for regression models to explain predicted continuous values.
• Cross-Modal Explanation - Generates local interpretable explanations for individual predictions across text, tabular, and image data.
• Multiclass Interpretations - Generates interpretations for multiple target labels to compare how different features influence various class predictions.
• Super-Pixel Segmentation - Groups pixels into contiguous regions to treat blocks of pixels as single features during model analysis.
• Interactive Visualization Rendering - Displays interactive visual representations of feature contributions and prediction probabilities within notebook environments.
• Numerical Binning - Provides utilities to convert continuous numerical variables into discrete bins to simplify feature influence explanations.
• Feature Interaction Visualizations - Renders model explanations as bar plots, interactive pages, or highlighted text to visualize feature weights.
• Explainable AI Libraries - Local interpretable model-agnostic explanations for any classifier.
• Model Explainability and Analysis - Explaining the predictions of any machine learning classifier.
• Model Interpretability - Explaining the predictions of any machine learning classifier.
• Model Interpretation - Explaining predictions of any machine learning classifier.
• Guardrails and AI Safety - Listed in the “Guardrails and AI Safety” section of the The Incredible Pytorch awesome list.

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