By the end of this chapter, you will be able to:
- Explain the importance of evaluating models
- Evaluate regression and classification models using a number of metrics
- Choose the right metric for evaluating and tuning a model
- Explain the importance of hold-out datasets and types of sampling
- Perform hyperparameter tuning to find the best model
- Calculate feature importance and explain why they are important
This chapter introduces us to how we can improve a model's performance by using hyperparameters and model evaluation metrics.
In the previous three chapters, we discussed the two types of supervised learning problems, regression and classification, followed by ensemble models, ...