from sklearn.preprocessing import KBinsDiscretizer enc = KBinsDiscretizer(n_bins=5, encode=’ordinal’, strategy=’uniform’) enc.fit_transform(X) What is the primary behavior of this configuration?

Question

Data Science — Hard

from sklearn.preprocessing import KBinsDiscretizer enc = KBinsDiscretizer(n_bins=5, encode=’ordinal’, strategy=’uniform’) enc.fit_transform(X) What is the primary behavior of this configuration?

Accepted Answer

The 'uniform' strategy in KBinsDiscretizer divides the feature range into bins of equal width. This differs from the 'quantile' strategy, which ensures each bin has an equal number of samples, and the 'kmeans' strategy, which uses clustering to determine bin boundaries. Uniform binning is sensitive to the distribution of the data and can result in bins with very few or no samples if the data is clustered.