How to Choose a Feature Selection Method For Machine Learning - MachineLearningMastery.com
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Numerical Input, Numerical Output
Numerical Output: Regression predictive modeling problem.
The most common techniques are to use a correlation coefficient, such as Pearson’s for a linear correlation, or rank-based methods for a nonlinear correlation.
Numerical Input, Categorical Output
(Numerical Input, Categorical Output)
Numerical Input, Categorical Output)
Categorical Output: Classification predictive modeling problem.
supervised and unsupervised,
supervised methods
wrapper
filter
intrinsic
1. Feature Selection Methods
Wrapper feature selection methods
create many models with different subsets of input features and select those features that result in the best performing model according to a performance metric
Filter feature selection methods
use statistical techniques to evaluate the relationship between each input variable and the target variable, and these scores are used as the basis to choose (filter) those input variables that will be used in the model.
intrinsic feature selection methods.
some machine learning algorithms that perform feature selection automatically as part of learning the model
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