Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Introduction

The video tutorial discusses Principal Component Analysis (PCA), a powerful technique for dimensionality reduction. It explains how PCA generates new features that are highly representative and does not require label information, making it an unsupervised method. The tutorial provides an example of reducing data from a three-dimensional space to a two-dimensional space, emphasizing the importance of selecting the best subspace to minimize information loss. The video concludes with a preview of the next topic, which will cover the criteria PCA uses to retain maximum information while reducing dimensions.

What is the primary purpose of Principal Component Analysis (PCA)?

• To increase the number of features in a dataset

• To reduce the dimensionality of data while retaining important information

• To classify data into different categories

• To generate labels for unlabeled data

Which of the following is true about PCA?

• It requires labeled data to function

• It is a supervised learning technique

• It generates new features that are highly representative

• It keeps the original features intact

In the context of PCA, what is a vector space?

• A space where data points are classified

• A mathematical space where data points are represented as vectors

• A space where data points are labeled

• A space where data points are reduced to a single dimension

What is the goal when reducing dimensions from three to two in PCA?

• To convert data into a single-dimensional space

• To increase the number of data points

• To find a subspace that represents the data with minimal information loss

• To eliminate all errors in data representation

What is the challenge when selecting a subspace in PCA?

• Maximizing the number of dimensions

• Minimizing the number of data points

• Maximizing the errors in data representation

• Minimizing the errors in data representation

How does PCA handle data points that do not lie on the subspace?

• It duplicates them to fit the subspace

• It removes them from the dataset

• It projects them orthogonally to the subspace and minimizes errors

• It ignores them completely

What will be discussed in the next video regarding PCA?

• The process of increasing data dimensions

• The criteria for retaining maximum information during dimensionality reduction

• The methods for generating new labels

• The techniques for supervised PCA