The dimensionality reduction method is the process of reducing the number of features under consideration by obtaining a set of principal variables. The Principal Component Analysis (PCA) technique is the most important technique used for dimensionality reduction. Here, we will talk about why we need dimensionality reduction, and we will also see how to perform the PCA technique in scikit-learn.
These are the reasons for having a high number of features while working on predictive analytics:
- It enables the simplification of models, in order to make them easier to understand and to interpret. There might be some computational considerations if you are dealing with thousands of features. It might be a good idea to reduce the number of features in order to save computational resources.
- Another reason is to avoid the "curse of dimensionality...