How does dimensionality reduction work in Machine Learning? Can you highlight some techniques that are commonly used?

Question

Anonymous · Accepted Answer

There are a few dimensionality reduction techniques:
1. PCA: PCA can help reduce the dimensionality while retaining majority of the
information in newly created independent features
2. Feature selection techniques: Several techniques such as Lasso, Tree-Based
models. Forward selection,backward elimination, Correlation plot, VIF, etc. can
help find relevant features for the model and then in turn reduce the
dimensionality

How does dimensionality reduction work in Machine Learning? Can you highlight some techniques that are commonly used?

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