Filter Methods
Filter methods are a type of feature selection technique in which the features are selected based on their statistical properties such as correlation, mutual information, or variance. These methods are applied before the model is trained and they aim to remove irrelevant or redundant features from the dataset. Filter methods are computationally efficient and can handle high-dimensional datasets. However, they do not take into account the interaction between features and may not always select the optimal subset of features for the model. Some common filter methods include Pearson correlation coefficient, Chi-squared test, and ANOVA F-test.
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