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.

What are some examples of criteria used in feature selection methods?

What is the significance of keeping the identity of original features in feature selection?

How does dimensionality reduction impact the interpretability of a model?