In machine learning, what is the term for the process of reducing the dimensionality of data while preserving as much information as possible?

A. Linear Regression B. K-Means Clustering C. Decision Tree D. Logistic Regression

A. The model performs well on the test data B. The model fits the training data perfectly C. The model fails to generalize D. The model is too simple to learn patterns

A. Support Vector Machine (SVM) B. K-Means Clustering C. Random Forest D. Linear Regression

A. To minimize errors on the training data B. To create a linear regression model C. To visualize data relationships D. To classify data points into categories