Feature Selection Based on Mutual Information Gain for Classification | Filter Method
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In this video, we will learn about the feature selection based on the mutual information gain for classification and regression. The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. Mutual information (MI) is a measure of the amount of information between two random variables is symmetric and non-negative,
🔊 Watch till last for a detailed description
03:43 What is mutual information?
09:55 Classification problem
17:47 Remove constant, quasi constant and duplicate features
24:58 Calculating MI
33:41 Build the model and compare the performance
38:33 Mutual information gain in regression
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