The correct answer is A. selectpercentile.
selectpercentile is a function in scikit-learn that selects only a subset of features belonging to a certain percentile. It takes two arguments: the percentile and the number of features to select. The percentile is a number between 0 and 100, and the number of features is an integer. For example, to select the top 10% of features, you would use the following code:
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from sklearn.feature_selection import selectpercentile
features = selectpercentile(features, 10)
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This would return a new DataFrame with only the top 10% of features.
featurehasher is a function in scikit-learn that converts features to a hash representation. This can be useful for dimensionality reduction or for speeding up feature selection algorithms.
selectkbest is a function in scikit-learn that selects the k best features according to a certain criterion. The criterion can be any function that takes a feature vector and returns a score. For example, you could use the following code to select the k best features according to their mean value:
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from sklearn.feature_selection import selectkbest
features = selectkbest(features, k=10, criterion=’mean’)
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This would return a new DataFrame with only the 10 features with the highest mean values.