Hands-On Machine Learning for Algorithmic Trading

Classification problems

Classification problems have categorical outcome variables. Most predictors will output a score to indicate whether an observation belongs to a certain class. In the second step, these scores are then translated into actual predictions.

In the binary case, where we will label the classes positive and negative, the score typically varies between zero or is normalized accordingly. Once the scores are converted into 0-1 predictions, there can be four outcomes, because each of the two existing classes can be either correctly or incorrectly predicted. With more than two classes, there can be more cases if you differentiate between the several potential mistakes.

All error metrics are computed from the breakdown of predictions ...