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=number correct predictionsnumber of samplesAccuracy = \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=number of true positivesnumber of true positives+number of false negativesSensitivity = \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=number of true positivesnumber of true positives+number of false positivesPrecision = \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=number of true negativesnumber of true negatives+number of false positivesSpecificity = \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!
Last modified 1yr ago
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