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.