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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!