In this chapter, you learned about many different aspects when it comes to choosing a suitable ML pipeline for a given problem.
Computational complexity, differences in training and scoring time, linearity versus non-linearity, and algorithm, specific feature transformations are valid considerations and it’s useful to look at your data from these perspectives.
You gained a better understanding of selecting suitable models and how machine learning pipelines work by practicing various use cases. You are starting to scratch the surface and this chapter was a good starting point to extend these skills.
In the next chapter, you will learn about optimizing hyperparameters and will be introduced to more advanced concepts, such as Bayesian-based hyperparameter optimization.
