Data Science and Machine Learning (Theory and Projects) A to Z - Features in Data Science: Feature Dimensionality Reduct

The video tutorial introduces dimensionality reduction, emphasizing its importance in managing data with high dimensions. It covers two main techniques: feature selection and feature extraction. Feature selection involves choosing a subset of original features based on certain criteria, while feature extraction creates new features from the original ones, reducing dimensionality without retaining original feature identities. The tutorial explains various criteria for feature selection, such as correlation scores and L1 regularization, and discusses the transformation process in feature extraction. The video sets the stage for deeper exploration of these methods in subsequent lessons.

Why is dimensionality reduction often necessary in data analysis?

• To make data more complex

• To reduce the demand for data

• To increase the number of features

• To eliminate the need for data

What is the primary goal of feature selection?

• To increase the dimensionality

• To remove all features

• To select a subset of existing features

• To create new features

Which of the following is a criterion used in feature selection?

• Correlation score

• Feature creation

• Feature transformation

• Feature elimination

How does feature extraction differ from feature selection?

• It creates new features from the original ones

• It maintains the original features

• It increases the number of features

• It eliminates all features

What is a key characteristic of feature extraction?

• Maintaining original feature identity

• Creating new features with reduced dimensions

• Increasing the number of dimensions

• Eliminating all original features

In which method is the original identity of features often not preserved?

• Feature selection

• Feature extraction

• Both feature selection and extraction

• Neither feature selection nor extraction

What is a common goal shared by both feature selection and feature extraction?

• Eliminating all features

• Reducing the number of dimensions

• Maintaining all original features

• Increasing the number of features