In this chapter, we will see how we can improve different models, and we will see how to modify model options. We will also learn how to use different models and see how we can remove noise by removing predictors that are not really needed for predictions. You will also understand how to prepare additional data for the models, and we will see how we can add additional fields. Finally, you see how how oversampling and undersampling different categories of an outcome variable can make it more likely that the model that you end up using actually better understands the data.
The following are the topics that will be covered in this chapter, and these are the ways in which models can be improved:
- Modifying model options
- Using different models
- Removing noise
- Doing additional data preparation
- Balancing data (oversampling/undersampling)