Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. ... Large numbers of input features can cause poor performance for machine learning algorithms. Dimensionality reduction is a general field of study concerned with reducing the number of input features.

What is dimensionality reduction example?

For example, maybe we can combine Dum Dums and Blow Pops to look at all lollipops together. Dimensionality reduction can help in both of these scenarios. There are two key methods of dimensionality reduction: Feature selection: Here, we select a subset of features from the original feature set.

How do you explain dimensionality reduction?

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.