LIME

LIME, or Local Interpretable Model-agnostic Explanations, is a powerful technique designed to explain the predictions of machine learning models. Developed by Marco Ribeiro in 2016, LIME provides insights into how models make decisions by approximating them with interpretable models in the vicinity of a given prediction. This method is particularly valuable in the realm of eXplainable AI (XAI), as it helps users understand complex models and their outputs. By focusing on local behavior, LIME enables practitioners to evaluate model robustness and fosters trust in machine learning applications across various domains.

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 ...

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...

LIME: explain Machine Learning predictions

Towards Data Science

LIME stands for Local Interpretable Model-agnostic Explanations. It is a method for explaining predictions of Machine Learning models, developed by Marco Ribeiro in 2016 [3]. In the following, we are…...

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…

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…

Edge 261: Local Model-Agnostic Interpretability Methods: LIME

TheSequence

In this issue: An overview of the LIME interpretability method. Meta AI’s controversial researcn about how interpretable neurons can negatively affect the accuracy of neural networks. The Alibi Explai...