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Confusion matrices

Confusion matrices are a great way to start exploring your model's accuracy. They provide the values needed to calculate a wide range of metrics, including sensitivity, specificity, and the F1-score.

You have built a classification model to predict if a person has a broken arm based on an X-ray image. On the testing set, you have the following confusion matrix:

Prediction: 0 Prediction: 1
Actual: 0 324 (TN) 15 (FP)
Actual: 1 123 (FN) 491 (TP)

This exercise is part of the course

Model Validation in Python

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Exercise instructions

  • Use the confusion matrix to calculate overall accuracy.
  • Use the confusion matrix to calculate precision and recall.
  • Use the three print statements to print each accuracy value.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate and print the accuracy
accuracy = (____ + ____) / (953)
print("The overall accuracy is {0: 0.2f}".format(accuracy))

# Calculate and print the precision
precision = (____) / (____ + ____)
print("The precision is {0: 0.2f}".format(precision))

# Calculate and print the recall
recall = (____) / (____ + ____)
print("The recall is {0: 0.2f}".format(recall))
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