Oftentimes, you will have to deal with a dataset containing a large number of features, many of which may be unnecessary. This is a typical problem where some features are very informative for the prediction, some are somehow related, and some are completely unrelated (that is, they only contain noise or irrelevant information). Keeping only the interesting features is a way to not only make your dataset more manageable but also have predictive algorithms work better instead of being fooled in their predictions by the noise in the data.
Hence, dimensionality reduction is the operation of eliminating some features of the input dataset and creating a restricted set of features that contains all of the information you need to predict the target variable in a more effective and reliable way. As mentioned previously, reducing the number of features usually...