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Unsupervised Learning with R by Erik Rodríguez Pacheco

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Chapter 6. Feature Selection Methods

In the previous chapter, we discussed the problems faced while working with high-dimensional datasets, sometimes called the curse of dimensionality. In this regard, we commented on how there are two ways to deal with the problem: by methods of dimensionality reduction, and through feature selection methods; in this chapter, we will focus on the latter.

This chapter aims to explain some techniques for feature selection, also known as variable selection or attribute selection. Feature selection is the process of selecting a subset of relevant features for use in model construction.

The key point is to choose a subset of relevant features of variables for modeling and to not use features that prove to be redundant ...

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