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Confusion Matrix Explained

A confusion matrix is a table that shows the performance of a classification model by listing the true positives, true negatives, false positives, and false negatives. It displays how often the model correctly or incorrectly classified observations into their actual classes. The document provides an example confusion matrix for a model classifying apples, oranges, and pears, showing the number of observations the model correctly and incorrectly classified into each class.

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Introduces the confusion matrix as a tool for assessing classification model performance, detailing true positives, negatives, false positives, and negatives.

Displays the classification performance, showing correct identifications of apples (50) and oranges (50) among other misclassifications.

Highlights correct classifications across classes: 75 for Apple, 105 for Orange, and 115 for Pear, indicating how many were correctly identified as not belonging to each class.

Details incorrect classifications, with 15 for Apple, 10 for Orange, and 70 for Pear, highlighting misidentified cases.

Examines false negatives, showing misclassifications with 55 for Apple, 30 for Orange, and 10 for Pear, focusing on cases wrongly marked as not belonging.

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Confusion Matrix Explained Samuel Bohman
What is a Confusion Matrix? A common method for describing the performance of a classification model consisting of true positives, true negatives, false positives, and false negatives. It is called a confusion matrix because it shows how confused the model is between the classes.
True Positives Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0 The model correctly classified 50 apples and 50 oranges.
True Negatives for Apple The model correctly classified 75 cases as not belonging to class apple. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
True Negatives for Orange The model correctly classified 105 cases as not belonging to class orange. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
True Negatives for Pear The model correctly classified 115 cases as not belonging to class pear. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
False Positives for Apple The model incorrectly classified 15 cases as apples. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
False Positives for Orange The model incorrectly classified 10 cases as oranges. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
False Positives for Pear The model incorrectly classified 70 cases as pears. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
False Negatives for Apple The model incorrectly classified 55 cases as not belonging to class apple. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
False Negatives for Orange The model incorrectly classified 30 cases as not belonging to class orange. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
False Negatives for Pear The model incorrectly classified 10 cases as not belonging to class pears. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0

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Confusion Matrix Explained

  • 1.
  • 2.
    What is aConfusion Matrix? A common method for describing the performance of a classification model consisting of true positives, true negatives, false positives, and false negatives. It is called a confusion matrix because it shows how confused the model is between the classes.
  • 3.
    True Positives Predicted class AppleOrange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0 The model correctly classified 50 apples and 50 oranges.
  • 4.
    True Negatives forApple The model correctly classified 75 cases as not belonging to class apple. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 5.
    True Negatives forOrange The model correctly classified 105 cases as not belonging to class orange. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 6.
    True Negatives forPear The model correctly classified 115 cases as not belonging to class pear. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 7.
    False Positives forApple The model incorrectly classified 15 cases as apples. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 8.
    False Positives forOrange The model incorrectly classified 10 cases as oranges. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 9.
    False Positives forPear The model incorrectly classified 70 cases as pears. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 10.
    False Negatives forApple The model incorrectly classified 55 cases as not belonging to class apple. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 11.
    False Negatives forOrange The model incorrectly classified 30 cases as not belonging to class orange. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0
  • 12.
    False Negatives forPear The model incorrectly classified 10 cases as not belonging to class pears. Predicted class Apple Orange Pear Actual class Apple 50 5 50 Orange 10 50 20 Pear 5 5 0