As mentioned earlier, the application we will be creating is a car-value predictor. Given a set of attributes tied to a car, one can predict if the price is a good deal or not. The attributes included in this example aren't a definitive list of attributes, nor should they be used as-is in a production environment. However, one could use this as a starting point for predicting a simple true-or-false answer based on several attributes.
As with previous chapters, the complete project code, sample dataset, and project files can be downloaded here: https://github.com/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET/tree/master/chapter04.
Diving into the trainer
As previously mentioned, for this binary classification application, we will be using the FastTree trainer.
FastTree is based on the Multiple Additive Regression Trees (MART) gradient boosting algorithm. Gradient boosting is a very popular technique, in which a series of trees...
