Question: Which of the following techniques is used for dimensionality reduction?
Options:
K-Means Clustering
Support Vector Machines
Principal Component Analysis
Decision Trees
Correct Answer: Principal Component Analysis
Solution:
Principal Component Analysis (PCA) is a widely used technique for reducing the dimensionality of data.
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.
No concepts available.
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