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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…

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…

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).

Squeezing LIME in a custom network

Towards Data Science

Machine and deep learning models are applied in a wide range of areas, spanning from fundamental research to industries and services. Their successful application to a wide diversity of problems has…

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…

Squeezing More out of LIME with Python

Towards Data Science

How to create global aggregations of LIME weights Continue reading on Towards Data Science

Unboxing the black box using LIME

Towards Data Science

As the complexity of a model increases, its accuracy increases but its interpretability reduces. Most of the complex machine learning models that are math-heavy are not easily interpretable and run…

Understanding how LIME explains predictions

Towards Data Science

In a recent post I introduced three existing approaches to explain individual predictions of any machine learning model. In this post I will focus on one of them: Local Interpretable Model-agnostic…

Idea Behind LIME and SHAP

Towards Data Science

In machine learning, there has been a trade-off between model complexity and model performance. Complex machine learning models e.g. deep learning (that perform better than interpretable models e.g…

Corona JS

Analytics Vidhya

Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”. Social Distancing is the way, and we're doing it right.

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...

Spark

Towards Data Science

Shilpa, a rookie data scientist, was in love with her first job with a budding startup: an AI-based Fintech innovation hub. While the startup started with the traditional single machine, vertical…

LightGBM

Towards Data Science

XGBoost reigned king for a while, both in accuracy and performance, until a contender rose to the challenge. LightGBM came out from Microsoft Research as a more efficient GBM which was the need of…

Colors

Matplotlib Tutorials

Colors Matplotlib has support for visualizing information with a wide array of colors and colormaps. These tutorials cover the basics of how these colormaps look, how you can create your own, and how ...

Limekiln – A little slice of heaven

Pete Warden's blog

Last week I was lucky enough to spend four days camped out amongst the redwoods at Limekiln State Park, on the California coast at Big Sur. With a waterfall at one end, the beach at the other and a cr...

Notorious RGB

Level Up Coding

This is the second installment in a series of articles covering image processing in Javascript. In the previous installment, I covered how to read the pixels of an image, process them on the GPU, and…...

torch.arccos

PyTorch documentation

Alias for torch.acos() .

Sugar

R-bloggers

Sugar cane – CC publicdomain by Miwok Day 13 of 30DayMapChallenge: « 5 minutes map (previously). Sugar cane fields in La Réunion. library(tidyverse) library(sf) RPG région La Réunion édition 2021 http...

Sugar

R-bloggers

Day 13 of 30DayMapChallenge : « 5 minutes map (previously). Sugar cane fields in La Réunion Continue reading: Sugar

torch.absolute

PyTorch documentation

Alias for torch.abs()

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…

Shazam

Towards Data Science

It is estimated that there are about 500,000 movies currently in existence. That’s more than anyone can watch in a lifetime. Moreover, not all people will like every movie that has ever been made…

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.

Interpretable and Explainable NER with LIME

Towards Data Science

While a lot of progress has been made to develop the latest greatest, state-of-the art, deep learning models with a gazillion parameters, very little effort has been given to explain the output of…