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Is TPR and recall same?
Recall and True Positive Rate (TPR) are exactly the same. So the difference is in the precision and the false positive rate. The main difference between these two types of metrics is that precision denominator contains the False positives while false positive rate denominator contains the true negatives.
What is TPR and FPR in machine learning?
TPR (True Positive Rate, also known as Recall) and FPR (False Positive Rate, probability of a false alarm) are metrics related to a classification model (classifier) predictive performance.
How does the ROC curve relate to TPR?
The ROC curve shows a trade-off between TPR and FPR (or false negatives and false positives). It plots TPR vs FPR at different thresholds. If we lower the classification threshold, we will classify more observations as positive, increasing True Positives. But this will cause even the false positives to increase.
Where does the ROC curve go in a classifier?
The ROC curve of a good classifier is closer to the top left of the graph. WHY? Because in a good classifier, we can achieve high TPR at low FPR (at optimal threshold. The FPR and TPR at different thresholds will be discussed in point b).
Which is the correct threshold for the ROC curve?
In the last graph example, where the threshold is at 1: All positives were miss-classified, therefore TPR = 0% All negatives were correctly classified, hence FPR = 0% Overall, we can see this is a trade-off. As we increase our threshold, we’ll be better at classifying negatives, but this is at the expense of miss-classifying more positives
How to calculate TPR curve for different classifiers?
For the NaiveBayes and Decision Trees tho, with prob/raw parameter speciefied in predict function I don’t get a single numeric vector but a vector of lists or matrix where probability for each class is specified (I guess), eg: