Dimensionality Reduction with PCA and LDA Using Sklearn
Dimensionality reduction refers to reducing the number of features in a dataset in such a way that the overall performance of the algorithms trained on the dataset is minimally affected. With dimensionality reduction, the training time of statistical algorithms can be significantly reduced, and data can be visualized more easily since it is not easy to visualize datasets in higher dimensions.
There are two main approaches used for dimensionality reduction: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In this chapter, you will study both of them.