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Confusion matrix

From WikiMD's Wellness Encyclopedia

Confusion Matrix[edit | edit source]

A confusion matrix is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. It is a tool often used in the field of machine learning and statistics to evaluate the accuracy of a classification model.

Structure of a Confusion Matrix[edit | edit source]

A confusion matrix is a square matrix that compares the actual target values with those predicted by the model. It is composed of four key components:

Actual \ Predicted Positive Negative
Positive True Positive (TP) False Negative (FN)
Negative False Positive (FP) True Negative (TN)
  • True Positive (TP): The number of instances that are correctly predicted as positive.
  • False Positive (FP): The number of instances that are incorrectly predicted as positive.
  • True Negative (TN): The number of instances that are correctly predicted as negative.
  • False Negative (FN): The number of instances that are incorrectly predicted as negative.

Metrics Derived from a Confusion Matrix[edit | edit source]

Several important metrics can be derived from the confusion matrix, which are crucial for understanding the performance of a classification model:

  • Accuracy: The proportion of the total number of predictions that were correct.
 : Accuracy = \( \frac{TP + TN}{TP + FP + TN + FN} \)
  • Precision: The proportion of positive identifications that were actually correct.
 : Precision = \( \frac{TP}{TP + FP} \)
  • Recall (also known as Sensitivity or True Positive Rate): The proportion of actual positives that were identified correctly.
 : Recall = \( \frac{TP}{TP + FN} \)
  • Specificity: The proportion of actual negatives that were identified correctly.
 : Specificity = \( \frac{TN}{TN + FP} \)
  • F1 Score: The harmonic mean of precision and recall, providing a balance between the two.
 : F1 Score = \( 2 \times \frac{Precision \times Recall}{Precision + Recall} \)

Applications[edit | edit source]

Confusion matrices are widely used in various fields such as:

  • Healthcare: To evaluate the performance of diagnostic tests.
  • Finance: To assess the accuracy of credit scoring models.
  • Marketing: To measure the effectiveness of customer segmentation models.

Limitations[edit | edit source]

While confusion matrices provide a comprehensive overview of a model's performance, they have limitations:

  • They do not provide a single measure of performance, which can make comparisons between models difficult.
  • They are not useful for imbalanced datasets, where the number of instances in different classes varies significantly.

See Also[edit | edit source]

References[edit | edit source]