Machine Learning Quick Reference

Dimensionality reduction

Let's talk about a scenario wherein we have been given a dataset from a bank and it has got features pertaining to bank customers. These features comprise customer's income, age, gender, payment behavior, and so on. Once you take a look at the data dimension, you realize that there are 850 features. You are supposed to build a model to predict the customer who is going to default if a loan is given. Would you take all of these features and build the model?

The answer should be a clear no. The more features in a dataset, the more likely it is that the model will overfit. Although having fewer features doesn't guarantee that overfitting won't take place, it reduces the chance of that. Not a bad deal, right?

Dimensionality ...