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In the machine learning process, feature selection is used to make the process more accurate. It also increases the prediction power of the algorithms by selecting the most critical variables and eliminating the redundant and irrelevant ones. This is why feature selection is important.

Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.

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Types of Feature Selection Methods in ML Chi-square Test. The Chi-square test is used for categorical features in a dataset. ... Fisher's Score. ... Correlation Coefficient. ... Dispersion Ratio. ... Backward Feature Elimination. ... Recursive Feature Elimination. ... Random Forest Importance.

A PDF displays the exact same content and layout no matter which operating system, device or software application it is viewed on. The PDF format allows you to integrate various types of content ? text, images and vector graphics, videos, animations, audio files, 3D models, interactive fields, hyperlinks, and buttons.

Feature selection is the process of isolating the most consistent, non-redundant, and relevant features to use in model construction. Methodically reducing the size of datasets is important as the size and variety of datasets continue to grow.

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Feature selection, as a dimensionality reduction. technique, aims to choose a small subset of the. relevant features from the original ones by re- moving irrelevant, redundant, or noisy features. Feature selection usually leads to better learn-