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How to compute precision/recall for multiclass-multilabel classification?

In case if you want to see the results directly: from sklearn.metrics import classification_report, confusion_matrix classification_report(y_test, y_pred) This ...
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What impact does increasing the training data have on the overall system accuracy?

I agree with @Serendipity: The performance of neural networks can continually improve as more and more data is provided to the model, BUT the capacity of the model must be adjusted to support the ...
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High recall or precision for information retrieval system?

Disease detection is about more than just recall. For instance, perfect recall is possible by diagnosing everyone with everything. Then it becomes a balance of missing cases vs false diagnoses and ...
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1 vote
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Precision Recall Curve Intepretation

This happens when the model has made false positive predictions with high confidence. As a simple example, assume we have 1000 test cases, of which 100 are positive and 900 are negative. Assume the 20 ...
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Calculating Area Under The Precision Recall Curve with multiclass

As a side note, there is a multi-class implementation of the average precision in the torchmetrics module that also supports different averaging policies. Note that ...
2 votes
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How can area under ROC (AUC) be bad when precision, recall, and accuracy are all good?

Wrangling the numbers, I get the following as percentages of the dataset: TP≈0.685037 FP≈0.288597 FN≈0.0164671 TN≈0.00989861 (So it seems you've swapped the positive class. That's not important for ...
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Relationship between recall and Precision-Recall curve

Have a look at the paper "The Relationship Between Precision-Recall and ROC Curves" by Jesse Davis and Mark Goadrich "[... a classifier] dominates in ROC space if and only if it ...
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