from sklearn import metrics
- Confusion Matrix - Shows details of classification inclusing TP,FP,TN,FN
- True Positive (TP), Actual class is 1 & prediction is also 1
- True Negative (TN), Actual class is 0 & prediction is also 0
- False Positive (FP), Acutal class is 0 & prediction is 1
- False Negative (FN), Actual class is 1 & prediction is 0
confusion_result=metrics.confusion_matrix(y_pred=pred, y_true=testY, labels=[0,1])
tp=confusion_result[1][1]
tn=confusion_result[0][0]
fp=confusion_result[0][1]
fn=confusion_result[1][0]
