classification , data science , evaluation
by Jepp Bautista
I will try my best to help you understand how to understand the most important evaluation metrics in machine learning. In this blog we will talk about all the terminologies related to a binary confusion matrix, precision, recall and many more. By the end of this blog I hope that you will never be confused by the confusion matrix or any other metrics in classification.
Very few mathematical mumbo-jumbos are present in this blog, unlike my previous blogs which you should also have a look at.
The Confusion Matrix
The confusion matrix (also called error matrix), is a visual representation of the performance of a classification model. It is a table with different combinations of a “predicted” class and an “actual” class. Below is a representation of the confusion matrix:
|Actual “Positive”||Actual “Negative”|
|Predicted “Positive”||True Positive||False Positive|
|Predicted “Negative”||False Negative||True Negative|
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