Advanced Concepts for Machine Learning Projects
In the previous chapter, we introduced a possible workflow for solving a real-life problem using machine learning. We went over the entire project, starting with cleaning the data, through training and tuning a model, and then lastly evaluating its performance. However, this is rarely the end of the project. In that project, we used a simple decision tree classifier, which most of the time can be used as a benchmark or minimum viable product (MVP). In this chapter, we cover a few more advanced concepts that can help with improving the value of the project and make it easier to adopt by the business stakeholders.
After creating the MVP, which serves as a baseline, we would like to improve the model’s performance. While attempting to improve the model, we should also try to balance underfitting and overfitting. There are a few ways to do so, some of which include:
- Gathering more data (observations)
- Adding more...
