Feature selection
In the previous chapter, we explored the components of a machine learning pipeline. A critical component of the pipeline is deciding which features will be used as inputs to the model. For many models, a small subset of the input variables provide the lion's share of the predictive ability. In most datasets, it is common for a few features to be responsible for the majority of the information signal and the rest of the features are just mostly noise.
It is important to lower the amount of input features for a variety of reasons including:
- Reducing the multi collinearity of the input features will make the machine learning model parameters easier to interpret. Multicollinearity (also collinearity) is a phenomenon observed with features in a dataset where one predictor feature in a regression model can be linearly predicted from the other's features with a substantial degree of accuracy.
- Reducing the time required to run the model...
