from sklearn.impute import KNNImputer imputer = KNNImputer(n_neighbors=5, weights=’distance’) imputer.fit_transform(X) What is the primary behavior of this configuration?

Question

Data Science — Hard

from sklearn.impute import KNNImputer imputer = KNNImputer(n_neighbors=5, weights=’distance’) imputer.fit_transform(X) What is the primary behavior of this configuration?

Accepted Answer

The KNNImputer with `weights='distance'` calculates the missing value as a weighted average of the 5 nearest neighbors. The weights are inversely proportional to the distance between the samples, meaning closer neighbors have a larger influence on the imputed value than more distant ones. This is more nuanced than simple mean imputation, as it accounts for local structure in the feature space.