> For the complete documentation index, see [llms.txt](https://docs.productizeml.com/productize-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.productizeml.com/productize-ml/train-and-evaluate/model-evaluation.md).

# 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!*