It is one of the simplest metrics that helps in finding how correct and how accurate the model is. It is used with classification problems wherein the output (or the class into which the dataset has been classified into) could belong any of the classes (one of the 2 in binary classification and one of the many in multi-class classification).
The confusion matrix is not a performance measure on its own, but most of the performance metrics are based on this matrix and the value this matrix gives out.
Terminologies associated with the confusion matrix:
TPR = True positives/ (True positives + False negatives)
TNR = True negatives/ (True negatives + False positives)
FPR = False positives / (False positives + True negatives)
FNR = False negatives / (False negatives + True positives)
The ideal situation is when the model gives 0 false positive values and 0 false negative values. But this wouldn’t be the case in real-life. The confusion matrix contains enough information so as to calculate precision and recall values as well.
The below image shows what a confusion matrix would look like while classifying an animal as a cat or a dog.
In this post, we understood about confusion matrix and how it can be used to determine the performance of a model.
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Thanks for sharing this article!! Machine learning is a branch of artificial intelligence (AI) and computer science that focus on the uses of data and algorithms. I came to know a lot of information from this article.