Accuracy, Recall, Precision, F1 Score in Python

Accuracy, Recall, Precision, F1 Score in Python

Precision and recall are two crucial yet misjudged topics in machine learning. It often pops up on lists of common interview questions for data science positions. Accuracy, Recall, Precision, F1 Score in Python

Precision and recall are two crucial yet misjudged topics in machine learning. It often pops up on lists of common interview questions for data science positions. Describe the difference between precision and recall, explain what an F1 Score is, how important is accuracy to a classification model? It’s easy to get confused and mix these terms up with one another so I thought it’d be a good idea to break each one down and examine why they’re important.

So, let’s set the record straight in this article.

For any machine learning model, we know that achieving a ‘good fit’ on the model is extremely crucial. This involves achieving the balance between underfitting and overfitting. However, when it comes to classification – there is another tradeoff that is precision-recall tradeoff.

Accuracy

The formula for accuracy is pretty straight forward.

Accuracy, Recall, Precision, F1 Score in Python

But when dealing with classification problems we are attempting to predict a binary outcome. Is it fraud or not? Will this person default on their loan or not? Etc. So what we care about in addition to this overall ratio is number predictions that were falsely classified positive and falsely classified negative, especially given the context of what we are trying to predict.We have breakdown the accuracy formula even more.

Accuracy, Recall, Precision, F1 Score in Python

Where TP = True Positive, TN = True Negatives, FP = False Positives and FN = False Negatives.

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