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How to use Chi2 test for feature selection?
The SelectKBest function with the chi2 test only works with categorical data. In fact, the result from the test only will have real meaning if the feature only has 1’s and 0’s.
What does high chi square mean for feature selection?
So high Chi-Square value indicates that the hypothesis of independence is incorrect. In simple words, higher the Chi-Square value the feature is more dependent on the response and it can be selected for model training.
What is the minimum value for feature selection?
When applied to Feature Selection, it is used as a test of independence to assess whether the assigned class is independent of a particular variable. The minimum value for this statistic is 0. The higher the Chi-Squared statistic, the more independent the variable.
How to select top variables in feature selection?
Under Feature Selection, select Top Features table to produce a report containing only top variables as indicated by the Number of features edit box. Feature importance plot is selected by default. This is a graphical representation of variable importance based on the measure selected in the Rank By drop-down menu.
When to use scikit-learn and categorical features?
While scikit-learn is a powerful powerful tool, sometimes it can be a pain in the neck. Using categorical features can be one such time, where you’re sure to miss the simple world of statsmodels regressions. We’ll be using the same dataset we’ve been using for all of our logistic regression and classification problems: scarf-knitting!
What does higher Chi2 mean in selectkbest?
So a higher value of chi2 statistic means “two categorical variables are dependent” and MORE USEFUL for classification. SelectKBest gives you the best two (k=2) features based on higher chi2 values. Thus you need to get those features that it gives, rather that getting the “other features” on the chi2 selector.
Which is example of backward selection in scikit-learn?
For example in backward selection, the iteration going from m features to m – 1 features using k-fold cross-validation requires fitting m * k models, while RFE would require only a single fit, and SelectFromModel always just does a single fit and requires no iterations. Ferri et al, Comparative study of techniques for large-scale feature selection.