Evaluating Model Performance: Calculating Accuracy, Precision, and Recall in Supervised Learning

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Evaluating the performance of machine learning models is essential to understand their effectiveness. In supervised learning, metrics such as accuracy, precision, and recall are commonly used to measure how well a model predicts outcomes.

Accuracy

Accuracy measures the proportion of correct predictions out of all predictions made. It is calculated by dividing the number of correct predictions by the total number of predictions.

While accuracy is useful, it can be misleading in imbalanced datasets where one class dominates. In such cases, other metrics provide better insights into model performance.

Precision and Recall

Precision indicates the proportion of true positive predictions among all positive predictions made by the model. It reflects the model’s ability to avoid false positives.

Recall, also known as sensitivity, measures the proportion of actual positives correctly identified by the model. It shows how well the model detects positive cases.

Calculating the Metrics

These metrics are derived from the confusion matrix, which summarizes true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN).

Formulas:

  • Accuracy: (TP + TN) / (TP + FP + TN + FN)
  • Precision: TP / (TP + FP)
  • Recall: TP / (TP + FN)