Model training
Once we split the data it is now time to run the training and test data through a series of models and assess the performance of a variety of models and determine how accurate each candidate model is. This is an iterative process and various algorithms might be tested until you have a model that sufficiently answers your question.
We will delve deeper into this step within later chapters. Plenty of material is provided on model selection in the rest of the book.
Candidate model evaluation and selection
After we train our model with various algorithms comes another critical step. It is time to select which model is optimal for the problem at hand. We don't always pick the best performing model. An algorithm that performs well with the training data might not perform well in production because it might have overfitted the training data. At this point in time, model selection is more of an art than a science but there are some techniques that are explored...