Dimensionality reduction

The dimensionality of a model is the number of independent attributes in the dataset. In order to reduce the complexity of the model, we need to reduce the dimensionality without sacrificing accuracy. When we work in complex multidimensional data, we need to select the features that can improve the accuracy of the technique we are using. Sometimes, we don't know whether the variables are independent or if they share some kind of relationship. We need criteria to find the best features and reduce the number of variables under consideration.

In order to address these problems, we will perform three techniques: Feature selection, Feature extraction, and Dimension reduction:

• Feature selection: We will select a subset of features in order to get better training times or improve the model accuracy. In data analysis, finding the best features for our problem is often guided by intuition, and we don't know the real value of a variable until we test it. However...