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What is the precision-recall curve?
A precision-recall curve shows the relationship between precision (= positive predictive value) and recall (= sensitivity) for every possible cut-off. The PRC is a graph with: • The x-axis showing recall (= sensitivity = TP / (TP + FN)) • The y-axis showing precision (= positive predictive value = TP / (TP + FP))
Is recall true positive rate?
In machine learning, the true positive rate, also referred to sensitivity or recall, is used to measure the percentage of actual positives which are correctly identified.
Why do we use precision and recall?
An alternative to using classification accuracy is to use precision and recall metrics. Precision quantifies the number of positive class predictions that actually belong to the positive class. Recall quantifies the number of positive class predictions made out of all positive examples in the dataset.
What is low recall for unbalanced dataset?
When predicting I get a low precision (0.47) for the minority class in the validation set; recall is 0.88. I tried to use several oversampling and under-sampling methods (performed on the training set) which did not improve the precision since the validation set is unbalanced as well to reflect the real class distribution.
How to evaluate accuracy on highly unbalanced data?
By prediction dumbly only good transactions (100 good transactions), you will have a 99% accuracy. Which can’t be good because you missed the fraudulent transaction. To evaluate unbalanced dataset, you should use metrics like precision, recall and f1-score for the given non-majority class.
When to use precision and recall in statistics?
Use precision and recall to focus on small positive class — When the positive class is smaller and the ability to detect correctly positive samples is our main focus (correct detection of negatives examples is less important to the problem) we should use precision and recall.
Which is the best metric to measure recall?
The precision and recall are both very high, because the performance on the positive class is good. TPR = TP/ (TP+FN) = 7/9 = 0.78, FPR = FP/ (FP+TN) = 0. Both metrics give similar results in this case. Now we switch the labels — 9 samples are negative and 1 is positive.