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Precision is the number of true positives over the number of predicted positives(PP), and recall is the number of true positives(TP) over the number of actual positives(AP) you get. I used the initials just to make it easier ahead. A true positive is when you predict a car in a place and there is a car in that place. A predicted positive is every car you ...


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Recall is the fraction of the relevant documents that are successfully retrieved. \begin{aligned}{\text{Recall}}&={\frac {tp}{tp+fn}}\,\end{aligned} Labels for a Class is equal to total examples which are actually belonging to the class: P = FN + TP Hence (FN + TP)* Recall = TP Precision is the fraction of retrieved documents that are relevant to the ...


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Precision and Recall are concepts that have been introduced in the field of information retrieval. Imagine you have a large set of documents, and you want to find the ones that are relevant to a particular issue. You can be sure to find all relevant documents if you simply return the whole lot -- you won't miss a single relevant document. So your recall is 1....


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It means precisely the same as true/false positive and true/false negative in the classic formulation of precision, recall, F-score for classification tasks. relevant and retrieved: true positive relevant and not retrieved: false positive not relevant and retrieved: false negative not relevant and not retrieved: true negative And yes, the relevance depends ...


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I understand the confusion and I wanted to refer to this (older post) because the metric really is unclear in the context of the SDNE paper. Perhaps I can try to explain it for future readers, in hopes that this makes sense. All this is my own interpretation, of course. SDNE is an autoencoder setup that outputs both node embeddings ($y_i$ vector for focal ...


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The second model has the same precision, but worse recall, than model 1. Therefore we would rather have model 1 than model 2. The third model has worse recall than model 1, and worse precision than model 1, therefore we would rather have model 1 than model 3. Thus, model 1 is the best object detection model.


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