2 votes

Why doesn't the high precision of neural network weights improve accuracy?

First, I have not read and do not have that book. That said, I would interpret that statement in the context of the intractability of guaranteeing that the optimization function will find global ...
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2 votes
Accepted

How to calculate the precision and recall given the predictions and targets in this case?

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 ...
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1 vote

Is my understanding of confidence (precision) correct?

The confidence equation you are referring to is the definition of precision in the Classification/pattern-recongition/information-retrieval contexts. You can visually understand the equation with the ...
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1 vote
Accepted

Equations for computing true positives and false positives when using object detection algorithms?

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 ...
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1 vote

What is meant by a "relevant document" in NLP?

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 ...
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  • 5,062
1 vote

What is meant by a "relevant document" in NLP?

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 ...
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1 vote

What is Precision@K for link prediction in graph embedding meaning?

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 ...
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  • 111
1 vote
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Given the precision and recall of this model, what can I say about it?

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 ...
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