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Module 1: Introduction

20267m

A test is applied to 1000 patients: 200 are sick, and 800 are healthy. It correctly detects 160 sick patients, while 40 healthy patients test positive. Construct the confusion matrix and calculate accuracy, precision, recall, and F1-score.

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Confusion matrix and metrics

There are 200 sick patients and 800 healthy patients. True positives = 160; false negatives = 200 − 160 = 40. False positives = 40; true negatives = 800 − 40 = 760.

Actual / Predicted Positive Negative Total
Sick TP = 160 FN = 40 200
Healthy FP = 40 TN = 760 800
Total 200 800 1000
  • Accuracy = ((TP+TN)/N=(160+760)/1000=\mathbf{92%})
  • Precision = (TP/(TP+FP)=160/200=\mathbf{80%})
  • Recall = (TP/(TP+FN)=160/200=\mathbf{80%})
  • F1 = (2PR/(P+R)=\mathbf{80%})

Accuracy measures overall correctness; precision measures reliability of positive predictions; recall measures how many actual sick patients were detected.

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