How to evaluate a classifier?
The classifier can be evaluated by building the confusion matrix. Confusion matrix shows the total number of correct and wrong predictions.
Confusion Matrix for class label positive(+VE) and negative(-VE)is shown below;
| Actual Class(Target) | |||||
| +VE | -VE | ||||
| Predicted Class (Model) | +VE | A =
True +VE |
B =
False -VE |
+VE
prediction |
P=A / (A+B) |
| -VE | C =
False +VE |
D = True -VE |
-VE
prediction |
D / (C + D) | |
| Sensitivity | Specificity | Accuracy =
A + D / (A + B + C + D) |
|||
| A / (A + C) | D / (B + D) | ||||
[quads id=1]
Accuracy:
Accuracy is the proportion of the total number of correct predictions.
e.g
Accuracy = A + D / (A + B + C + D)
Error-Rate:
Error Rate = 1 – Accuracy
+VE predictions:
+VE predictions are the proportion of the total number of correct positive predictions.
+VE predictions = A / (A+B)
[quads id=2]
-VE predictions:
-VE predictions are the proportion of the total number of correct negative predictions.
-VE predictions = D / (C + D)
Precision:
Precision is the correctness that how much tuple are
- +VE and classifier predicted them as +VE
- -VE and classifier predicted them as -VE
Precision = A / P
Recall:
Recall = A / Real positive
Sensitivity (Recall):
Sensitive is the total True +VE rate.
The correction of the actual positive cases that are correctly identified.
Sensitivity (Recall) = A / (A + C)
F-Measure:
F-Measure is harmonic mean of recall and precision.
F-Measure = 2 * Precision * Recall / Precision + Recall
Specificity:
Specificity is true -VE rate.
Specificity is the proportion of the actual -VE cases that are correctly identified.
Specificity = D / (B + D)
Note: Specificity of one class is same as the sensitivity of the other class.
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