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Why is dimension reduction important?

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Dimensionality reduction is the process of reducing the number of features in a dataset. This is important mainly in the case when you want to reduce variance in your model (overfitting).

Wikipedia states four advantages of dimensionality reduction (see here):

It reduces the time and storage space required

Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model

It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D

It avoids the curse of dimensionality

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Dimension reduction is the process which is used to reduce the number of random variables under considerations.

Dimension reduction can be divided into feature selection and extraction.

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