## Model evaluation terms

**Confusion matrix:**
In the context of a binary classification algorithm a confusion matrix
shows a cross-tabulation of how many of the actual positive/negative results the model has
predicted to be positive/negative.

**False positive:**
A false positive indicates an instance where a ML model predicts a positive outcome, but the prediction is wrong because a negative outcome occurs.

**False positive Rate:**
The false positive rate represents the proportion of predicted positive outcomes that are incorrect.

**True positive:**
In the context of predictions made by a classification model, a true positive represents a case where the model predicts a ‘positive’ outcome, and the real outcome is also positive.

**True positive rate:**
The true positive rate represents the proportion of positive outcomes that were predicted as positive by an ML model.