Model Evaluation

You will learn: how to assess to evaluation of ML models.

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!