What is a confusion matrix?

The confusion matrix is a 2×2 table that contains 4 outputs provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Confusion matrix.

A dataset used for performance evaluation is called a test data set. It should contains the correct labels and predicted labels.

The predicted labels will exactly the same if the performance of a binary classifier is perfect.

The predicted labels usually match with part of the observed labels in real-world scenarios.

A binary classifier predicts all the data instances of a test data set as either positive or negative. This produces for outcomes-

  • True positive (TP) – Correct positive prediction
  • positive (FP) – Incorrect positive prediction
  • True negative (TN) – Correct negative prediction
  • False negative (FN) – Incorrect negative prediction

Basic measures derived from the confusion matrix-

  1. Error Rate – (FP + FN) / (P + N)
  2. Accuracy – (TP + TN) / (P + N)
  3. Sensitivity (Recall or True positive rate) – TP / P
  4. Specificity (True negative rate) – TN / N
  5. Precision (Positive predicted value) – TP / (TP + FP).

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