What is filter method in data mining?
Data filtering is the process of choosing a smaller part of your data set and using that subset for viewing or analysis. Filtering is generally (but not always) temporary – the complete data set is kept, but only part of it is used for the calculation.
What are the filter methods?
Filter methods measure the relevance of features by their correlation with dependent variable while wrapper methods measure the usefulness of a subset of feature by actually training a model on it. Filter methods are much faster compared to wrapper methods as they do not involve training the models.
How to use univariate filters for feature selection?
Another approach to feature selection is to pre-screen the predictors using simple univariate statistical methods then only use those that pass some criterion in the subsequent model steps. Similar to recursive selection, cross-validation of the subsequent models will be biased as the remaining predictors have already been evaluate on the data set.
How are multivariate filter methods different from other filters?
Multivariate filter methods, on the other hand, evaluate the entire feature space. They take into account features in relation to other ones in the dataset. These methods are able to handle duplicated, redundant, and correlated features.
How to use the filter based feature selection module?
The module includes correlation methods such as Pearson correlation and chi-squared values. When you use the Filter Based Feature Selection module, you provide a dataset and identify the column that contains the label or dependent variable. You then specify a single method to use in measuring feature importance.
What are the advantages of using filter methods?
This is one of the biggest advantages of filter methods. Features selected using filter methods can be used as an input to any machine learning models. Another advantage of filter methods is that they are very fast. Filter methods are generally the first step in any feature selection pipeline.