# Model Evaluation

## Classification metrics

* **Accuracy**: computes the number of correct predictions divided by the total number of samples.

  $$Accuracy = \frac{number \space correct \space predictions}{number \space of \space samples}$$
* **Sensitivity**: also known as **recall**, is computed as the fraction of true positives that are correctly identified.

  $$Sensitivity = \frac{number \space of \space true \space positives}{number \space of \space true \space positives + number \space of \space false \space negatives}$$
* **Precision**: computed as the fraction of retrieved instances that are relevant.

  $$Precision = \frac{number \space of \space true \space positives}{number \space of \space true \space positives + number \space of \space false \space positives}$$
* **Specificity**: computed as the fraction of true negatives that are correctly identified.

  $$Specificity = \frac{number \space of \space true \space negatives}{number \space of \space true \space negatives + number \space of \space false \space positives}$$

🚧 *This section is still under construction!*