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confusion matrix may confuse you sometime but its very simple by definition-- it is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values.It is extremely useful for measuring Recall, Precision, Specificity, Accuracy and most importantly AUC-ROC Curve.

This video is an explanation of confusion matrix, Precision recall and F1- Score with 3D visualization hope you will like it. It is very helpful if you are

1. Confused about confusion matrix
2. want to know what is true positive, false positive, true negative and False negative
3. Type 1 and type 2 errors

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