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What is SelectKBest in machine learning?
SelectKBest then simply retains the first k features of X with the highest scores. So, for example, if you pass chi2 as a score function, SelectKBest will compute the chi2 statistic between each feature of X and y (assumed to be class labels). A small value will mean the feature is independent of y.
What does F mean in regression?
The F value in regression is the result of a test where the null hypothesis is that all of the regression coefficients are equal to zero. Basically, the f-test compares your model with zero predictor variables (the intercept only model), and decides whether your added coefficients improved the model.
How does selectkbest ( ) perform feature selection?
SelectKBest (f_classif, k), where k is the number of features to select, is often used for feature selection, however, I am having trouble finding descriptive documentation on how it works. A sample of how this works is below:
How does selectkbest, filters out the rest & keeps the best?
How does SelectKBest, filters out the rest & keeps the best? SelectKBest is a popular feature selection technique that is used to retain relevant features and drop unwanted features. But how many of us actually know what happens behind the scenes when SelectKBest gives you a set of K relevant features?
How does selectkbest work in data science Stack Exchange?
And yes, f_classif and chi2 are independent of the predictive method you use. The k parameter is important if you use selector.fit_transform (), which will return a new array where the feature set has been reduced to the best ‘k’. Thanks for contributing an answer to Data Science Stack Exchange!
How does the selectkbest class in Python work?
The SelectKBest class just scores the features using a function (in this case f_classif but could be others) and then “removes all but the k highest scoring features”. So its kind of a wrapper, the important thing here is the function you use to score the features.