Practical Data Science using Python - Principal Component Analysis - Concepts

The video tutorial introduces Principal Component Analysis (PCA) as a technique for dimensionality reduction, crucial for handling high-dimensional data in machine learning. It covers the importance of data standardization, the role of covariance matrices, eigenvectors, and eigenvalues in PCA, and how to recast data using principal components. The tutorial also discusses the challenges of high-dimensional data, such as the curse of dimensionality, and the benefits of reducing dimensions for model accuracy and visualization.

What is the primary goal of Principal Component Analysis?

• To increase the number of features in a dataset

• To reduce the dimensionality of a dataset

• To eliminate all noise from a dataset

• To improve the visualization of data

Why is dimensionality reduction important in machine learning?

• It increases the complexity of models

• It helps in handling high-dimensional data efficiently

• It ensures all features are used in the model

• It guarantees 100% accuracy in predictions

What is the 'curse of dimensionality'?

• A situation where high-dimensional data leads to various problems

• A phenomenon where data becomes easier to interpret

• A technique to improve model accuracy

• A method to increase the number of features

How does high-dimensional data affect model performance?

• It reduces the noise in the data

• It always improves model accuracy

• It simplifies the model training process

• It can lead to overfitting and reduced generalizability

What is the difference between feature selection and feature extraction?

• Feature extraction involves creating new features, while feature selection selects existing ones

• Both involve selecting existing features

• Feature selection involves creating new features, while feature extraction selects existing ones

• Both involve creating new features

In PCA, what is the purpose of creating new features?

• To eliminate all features

• To ensure all features are equally important

• To retain the most variance from the original dataset

• To increase the number of features

What is a basis vector in the context of PCA?

• A vector that increases the dimensionality of the data

• A vector that eliminates noise from the data

• A vector that defines the direction of a feature in the coordinate system

• A vector that represents the original dataset

Why is it important to retain the most variance in PCA?

• To increase the number of features

• To ensure the model is complex

• To make the data more difficult to interpret

• To avoid losing important information from the original dataset

What does PCA aim to achieve with a new coordinate system?

• To ensure all features are equally weighted

• To create a system where the most variance is retained

• To increase the number of dimensions

• To eliminate all features

How does PCA help in visualizing high-dimensional data?

• By reducing dimensions to a more manageable number

• By increasing the number of dimensions

• By eliminating all features

• By ensuring all features are visible