LIME

LIME, or Local Interpretable Model-agnostic Explanations, is a powerful technique in the field of explainable artificial intelligence (XAI). It aims to provide insights into the predictions made by complex machine learning models by approximating them with simpler, interpretable models locally around a specific prediction. This approach allows users to understand why a model made a particular decision, enhancing trust and transparency. LIME is particularly valuable in applications where understanding model behavior is crucial, such as healthcare and finance. As the demand for explainable AI grows, LIME serves as an essential tool for evaluating and interpreting machine learning models effectively.

Understanding LIME

Towards Data Science

Local Interpretable Model-agnostic Explanations (LIME) is a Python project developed by Ribeiro et al. [1] to interpret the predictions of any supervised Machine Learning (ML) model. Most ML…

What’s Wrong with LIME

Towards Data Science

Local Interpretable Model-agnostic Explanations (LIME) is a popular Python package for explaining individual model’s predictions for text classifiers or classifiers that act on tables (NumPy arrays…

LIME Light: Illuminating Machine Learning Models in Plain English

The Pythoneers

Machine learning models have made significant advancements in various domains, from healthcare to finance and natural language processing. However, the predictions generated by these models are often ...

A Deep Dive on LIME for Local Interpretations

Towards Data Science

LIME is the OG of XAI methods. It allows us to understand how machine learning models work. Specifically, it can help us understand how individual predictions are made (i.e. local interpretations).

Local Surrogate (LIME)

Christophm Interpretable Machine Learning Book

Local surrogate models are interpretable models that are used to explain individual predictions of black box machine learning models. Local interpretable model-agnostic explanations (LIME) 50 is a pap...

ML Model Interpretability — LIME

Analytics Vidhya

Lime is short for Local Interpretable Model-Agnostic Explanations. Each part of the name reflects something that we desire in explanations. Local refers to local fidelity i.e "around" the instance bei...

Interpretable Machine Learning for Image Classification with LIME

Towards Data Science

Local Interpretable Model-agnostic Explanations (LIME) provides explanations for the predictions of any ML algorithm. For images, it finds superpixels strongly associated with a class label.

LIME — Explaining Any Machine Learning Prediction

Towards AI

The main goal of the LIME package is to explain any black-box machine learning models. It is used for both classification and regression problems. Let’s try to understand why we need to explain…

LIME : Explaining Machine Learning Models with Confidence

Python in Plain English

LIME: Explaining Machine Learning Models with Confidence Photo by Yousz on Pixabay ‍Machine learning models have become increasingly complex and accurate over the years, but their opacity remains a s...

Build a LIME explainer dashboard with the fewest lines of code

Towards Data Science

In an earlier post, I described how to explain a fine-grained sentiment classifier’s results using LIME ( Local Interpretable Model-agnostic Explanations). To recap, the following six models were…

How the LIME algorithm fails

Depends on the definition

You maybe know the LIME algorithm from some of my earlier blog posts. It can be quite useful to “debug” data sets and understand machine learning models better. But LIME is fooled very easily.

Instability of LIME explanations

Towards Data Science

In this article, I’d like to go very specific on the LIME framework for explaining machine learning predictions. I already covered the description of the method in this article, in which I also gave…