Feature Selection
Feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. It is an important step in the machine learning pipeline as it helps to improve model performance, reduce overfitting, and increase interpretability. Feature selection can be done through various methods such as filter methods, wrapper methods, and embedded methods. Filter methods use statistical tests to rank features based on their correlation with the target variable. Wrapper methods use a search algorithm to evaluate different subsets of features based on their performance on a specific model. Embedded methods incorporate feature selection as part of the model training process. Feature selection is particularly important in high-dimensional datasets where the number of features is much larger than the number of observations.
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