Dimensionality Reduction Techniques: What is PCA and How Does it Work?

Dimensionality Reduction Techniques: What is PCA and How Does it Work?

Question: Which of the following is an example of a dimensionality reduction technique?

A. Principal component analysis (PCA)

B. AdaBoost

C. Support vector machine (SVM)

D. K-nearest neighbors (KNN)

Answer: A. Principal component analysis (PCA)

Explanation:

Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction. It works by transforming a dataset with potentially correlated variables into a new set of uncorrelated variables called principal components. These principal components are ordered by the amount of variance they explain in the original data, allowing you to retain the most important information while reducing the number of dimensions.

Here's why the other options are incorrect:

• AdaBoost is a boosting algorithm used for improving the accuracy of machine learning models.* Support vector machine (SVM) is a supervised learning model used for classification and regression tasks.* K-nearest neighbors (KNN) is a simple algorithm used for both classification and regression, relying on the proximity of data points.