Understanding the effect of irrelevant features
Feature selection is also known as variable or attribute selection. It is the method by which you can automatically or manually select a subset of specific features useful to the construction of ML models.
It's not necessarily true that more features lead to better models. Irrelevant features can impact the learning process, leading to overfitting. Therefore, we need some strategies to remove any features that might adversely affect learning. Some of the advantages of selecting a smaller subset of features include the following:
- It's easier to understand simpler models: For instance, feature importance for a model that uses 15 variables is much easier to grasp than one that uses 150 variables.
- Shorter training time: Reducing the number of variables decreases the cost of computing, speeds up model training, and perhaps most notably, simpler models have quicker inference times.
- Improved generalization by reducing...
