In Python, can I get 100 best features out of 200k by performing Linear Discriminant Analysis on data having 2 classes?
Although LDA is used for multi-class problems, it can be used in binary classification problems.
You can use LDA for dimensionality reduction which aims to reduce the number of features. Feature selection on the other hand is the process of selecting a subset of features from a set of features.
So it is a kind of feature extraction and not feature selection. This means LDA will create a new set of features and not select the best features.
In essence, the original features no longer exist and new features are constructed from the available data that are not directly comparable to the original data [1].
Check this link for further reading
[1] Linear Discriminant Analysis for Dimensionality Reduction in Python