Quoting the curse of dimensionality (https://en.wikipedia.org/wiki/Curse_of_dimensionality), large number of features are computationally expensive. One way of reducing the number of features is by manually choosing and ignoring certain features. However, identification of the same features (represented differently) or highly correlated features is laborious when we have a huge number of features. Dimensionality reduction is aimed at reducing the number of features in the data while still retaining its variability.
Say, we have a dataset of housing prices and there are two features that represent the area of the house in feet and meters; we can always drop one of these two. Dimensionality reduction is very useful when dealing with text where the number of features easily runs into a few thousands.
In this recipe, we'll be looking into Principal Component Analysis (PCA) as a means to reduce the dimensions of data that is meant for both supervised...