What is Interpretable Machine Learning - ML Explainability - with Python LIME Shap Tutorial

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In this ML video, We'll learn about Interpretable Machine Learning which otherwise is known as Machine Learning Explainability and Explainable AI. Along with Python Examples

00:00 Introduction - Outline
01:13 Credits
02:57 What is Interpretable Machine Learning?
04:12 Why is Machine Learning Explainability Required?
12:12 How is IML relevant to me?
16:45 Types of IML
19:40 LIME , Advantages and Disadvantages of LIME with Python Tutorial
30:50 SHAP , Advantages and Disadvantages of SHAP

  1. 1littlecoder
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Комментарии
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Nice video. I am doing covid detection from xray images and want to use Lime to explain the predictions of my trained model. How can we import our model to IBM watson for the explainability please?

soothingrelaxation
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Thank you. Hope you would've provided the notebook link too.

arskas
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Hello very good tuto but seems taht LIME can not work with OCSVM as it doesn't provide prediction score

MameNgoneFAYE
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25:01 Why probability of NOT SURVIVE is predicted as 0 when there is at least a contribution of 0.03+0.01 ?(the blue probabilities)

bryanparis
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why we not take traning data, how can i know the actual records we conver value into number the perfrom lime, my question is there is one record the actual value not converted how can i find that recored why misclassification is happens

abhishekprakash
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What is 0.25 below Sex_male plz reply 24:43

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