The correct answer is: B. The process of reducing the dimensionality of data while retaining important information.
Feature extraction is a process of selecting a subset of features from the original dataset that are most relevant to the target variable. This can be done using a variety of methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), and k-means clustering.
The goal of feature extraction is to reduce the dimensionality of the data while retaining as much information as possible about the target variable. This can be useful for a variety of tasks, such as data visualization, machine learning, and data mining.
Option A is incorrect because data imputation is the process of filling in missing values in a dataset.
Option C is incorrect because data normalization is the process of transforming data so that it has a mean of 0 and a standard deviation of 1.
Option D is incorrect because data imputation is the process of filling in missing values in a dataset.