Model metrics

During the construction of the model we might need to make design choices about which kinds of error the model will make, like prioritizing minimizing false positives.

Accuracy: measure of how many instances the model got right.

$$accuracy=\frac{truepositive+truenegative}{truepositive+truenegative+falsepositive+falsenegative}$$

PPV: Positive predictive value: how may true positives the model came up from the things it labeled positive.

$$positivepredictivevalue=\frac{truepositive}{truepositive+falsepositive}$$

Sensitivity: what percentage did the model correctly find.

$$sensitivity=\frac{truepositive}{truepositive+falsenegative}$$

Specificity: what percentage did the model correctly reject.

$$specificity=\frac{truenegative}{truenegative+falsepositive}$$

Sensitivity and specificity suffer a trade off between each other.