Which of the following techniques is used for dimensionality reduction?
Practice Questions
Q1
Which of the following techniques is used for dimensionality reduction?
K-Means Clustering
Support Vector Machines
Principal Component Analysis
Decision Trees
Questions & Step-by-Step Solutions
Which of the following techniques is used for dimensionality reduction?
Step 1: Understand what dimensionality reduction means. It is the process of reducing the number of features (or dimensions) in a dataset while retaining important information.
Step 2: Learn about different techniques used for dimensionality reduction. One common technique is Principal Component Analysis (PCA).
Step 3: Recognize that PCA transforms the original data into a new set of variables called principal components, which capture the most variance in the data.
Step 4: Identify that PCA helps simplify the dataset, making it easier to analyze and visualize without losing significant information.
