In this chapter, we are going to apply different algorithms with the aim of obtaining a good model using combinations of our predictors. The most common algorithm that's used in credit risk applications, such as credit scoring and rating, is logistic regression. In this chapter, we will see how other algorithms can be applied to solve some of the weaknesses of logistic regression.
In this chapter, we will be covering the following topics:
- Logistic regression
- Regularized methods
- Testing a random forest model
- Gradient boosting
- Deep learning in neural networks
- Support vector machines
- Ensembles
- Automatic machine learning
