Principal Component Analysis (PCA)

[hankehly]

PCA can reduce dimensionality and multicollinearity by transforming the original features into a smaller set of uncorrelated components.

Method: PCA reduces the dimensionality of the dataset while retaining most of the variability in the data. It can highlight which original features contribute most to the variance in the dataset.

How to Use: After performing PCA, analyze the components to see if multiple features significantly contribute to the first few principal components, indicating they provide similar information.

Tool: The PCA class from sklearn.decomposition can be used for PCA in Python.

[hankehly]

Consider adding PCA to your pipeline to reduce training time