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Model Evaluation, MUHAMAD FARIS FARHAN BIN FAUZI - 2019579545, MUHAMMAD…
Model Evaluation
Sensitivity & Specificity
Used for: Medical data, fraud detection
Significant majority of the negative class and minority of the
positive class
Sensitivity
True Positive recognition rate: sensitivity = TP/P
Specificity
True Negative recognition rate: sensitivity = TN/N
Accuracy & Error Rate
Error rate = FP + FN / TP + TN + FP + FN
Classifier Accuracy = TP + TN / TP + TN + FP + FN
Example of confusion matrix
Precision and Recall
Precision: exactness
Precision = TP/(TP + FP)
= TP/P'
Recall: completeness
Recall TP/(TP+FN) = TP/P
F and Fß measures
F = 2 x precision x recall / precision + recall
Fß = (1 + ß^2) x precision x recall / ß^2 x precision + recall
What is confusion matrix?
The confusion matrix is a useful tool for determining how well your classifier recognizes tuples of various classes.
TP and TN indicate when the classifier is correct, whereas FP and FN indicate when the classifier is incorrect.
MUHAMAD FARIS FARHAN BIN FAUZI - 2019579545
MUHAMMAD ISLAH HAKIMI BIN MOHD IDRIS - 2019361521
MUHAMMAD HAIKAL BIN MUHAMMAD JOHAN - 2019322189
