LIME Local Explanations - Interactive Visualization

LIME (Local Interpretable Model-Agnostic Explanations) explains any classifier's prediction by approximating it locally with a simple linear model. It generates perturbed versions of the input, queries the black-box model for predictions on each, and fits a weighted linear regression where nearby perturbations receive higher weight. The linear model's coefficients explain which features drove this specific prediction.

• Watch LIME generate hundreds of perturbed samples around a specific data point and query the black-box model on each
• See the decision boundary of the complex model vs the simple linear approximation in the local neighborhood
• Read off feature importance from LIME: positive coefficients push toward the predicted class, negative away from it
• Understand proximity weighting: perturbations closer to the original point receive exponentially higher weight in the local fit
• Learn the limitation: LIME explanations are local - the same features may have opposite effects at a different data point

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.