LIME is a new technique that explains predictions of any machine learning classifier and has been shown to increase human trust and understanding. Explaining predictions Figure 1 from paper

Jan 01, 2021 Jan 20, 2021 LIME for Text Classification Model From the experience of using LIME for image model explanation, we know creating Surrogate Regression Training dataset is the key, so let’s explore how we can

Feb 02, 2021 Feb 02, 2021 LIME explanation as to why the predicted value is 4.50 for this regression problem Building a Digit Classifier. Install tensorflow using the following command on cmd. Firstly, make sure that you have python and pip installed and set as environment variable.. pip install tensorflow

Apr 02, 2016 Apr 02, 2016 Lime: how we get explanations. 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., we want the explanation to really reflect the behaviour of the classifier around the instance being predicted

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Jan 01, 2021 Jan 01, 2021 Create LIME text explainer and explain model classification on the 10th sentence in test dataset. The text explainer will find the top

Jul 11, 2018 Jul 11, 2018 LIME is a great tool to explain what machine learning classifiers (or models) are doing. It is model-agnostic, leverages simple and understandable idea’s and does not require a lot of effort to run. As always, even when using LIME, it is still important to correctly interpret the output

Nov 04, 2018 Nov 04, 2018 LIME is a python library that tries to solve for model interpretability by producing locally faithful explanations. Below is an example of one such explanation for a text classification problem. Example of an explanation by LIME for a binary classification model (atheism/Christian). The words (features) highlighted in blue support atheism

lime. This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations)

LIME explains an AI model. The explainable AI method LIME (Local Interpretable Model-agnostic Explanations) helps to illuminate a machine learning model and to make its predictions individually comprehensible. The method explains the classifier for a specific single instance and is therefore suitable for local explanations

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Aug 13, 2016 Aug 13, 2016 In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non

Jan 01, 2021 Jan 01, 2021 The classification accuracy of tweet posting locations through a classifier employing BERT is evaluated and the analysis of factors using LIME [14] which affected classification results is made. LIME is known as a method to extract the group of words that contributed to the results of a classification prediction and is used not only for text