Model complexity and assessment is a must-do step toward building a successful data science system. There are lots of tools that you can use to assess and choose your model. In this chapter, we are going to address some of the tools that can help you to increase the value of your data by adding more descriptive features and extracting meaningful information from existing ones. We are also going to address other tools related optimal number features and learn why it's a problem to have a large number of features and fewer training samples/observations.
The following are the topics that will be explained in this chapter:
- Feature engineering
- The curse of dimensionality
- Titanic example revisited—all together
- Bias-variance decomposition
- Learning visibility
