This project aims to evaluate the strength of features in the diabetes dataset. Multiple feature selection techniques are used, including Pearson correlation coefficient, mutual information, random forest feature importance, and recursive feature elimination.
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This project aims to evaluate the strength of features in the diabetes dataset. Multiple feature selection techniques are used, including Pearson correlation coefficient, mutual information, random forest feature importance, and recursive feature elimination.
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omartgabr/Diabetes-Feature-Selection-Analysis
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This project aims to evaluate the strength of features in the diabetes dataset. Multiple feature selection techniques are used, including Pearson correlation coefficient, mutual information, random forest feature importance, and recursive feature elimination.
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