The confusion matrix
I’ve chosen to dedicate an entire section of this chapter to the confusion matrix (don’t be discouraged by the name) because I believe, among the various methods for evaluating model effectiveness, that the matrix closely aligns with a trader’s way of thinking better than any other. It’s straightforward, avoids complex statistical formulas, and is highly effective.
A confusion matrix is a table that’s used to evaluate the performance of a classification model. It summarizes the model’s predictions compared to actual outcomes by displaying four key components (Table 11.6):
|
Component |
Description |
|
True positives (TPs) |
Represents correct positive predictions |
|
True negatives (TNs) |
Represents correct negative predictions |
| ... |
