Accuracy: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Accuracy = '''Accuracy''' is one of the most commonly used metrics to evaluate the performance of a classification model in machine learning. It tells us the proportion of total predictions that were correct. == Definition == :<math> \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} </math> Where: * '''TP''' = True Positives * '''TN''' = True Negatives * '''FP''' = False Positives * '''FN''' = False Negatives Accuracy answers the question: '''"Out of all predict..." |
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accuracy in machine learning, classification accuracy, accuracy formula, model evaluation metric, accuracy vs precision, confusion matrix accuracy, balanced data accuracy | accuracy in machine learning, classification accuracy, accuracy formula, model evaluation metric, accuracy vs precision, confusion matrix accuracy, balanced data accuracy | ||
[[Category:Artificial Intelligence]] | |||