Confusion Matrix

Confusion matrix is a matrix that is used to calculate the accuracy of classification model.It can also be defined as a table in which original and prediction results are present.

Below is the table

 

1- Positive 

0 - Negative

TP - True Positive

TN - True Negative

FN - False Negative (Type 2 Error)

FP - False Positive (Type 1 Error)

Accuracy = (TP + TN)/(TP + TN + FP + FN)

Recall = TP/(TP + FN) - Out of all positive classes how much we have predicted positive

Precision = TP/(TP +FP) - Out of all predictive positive classes, how many are actually positive

F-Measure = (2 * Recall * Precision)/(Recall + Precision)

F-Measure is the harmonic mean of recall and precision.F-Measure helps to measure recall and precision at same time as it would be hard to interpret the better model when there is high recall and low precision and vice versa due to which F-Measure is used

Sensitivity

Specificity

ROC Curve

AUC