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What's the meaning of recall of a classifier, e.g. bayes classifier? please give an example.
for example, the Precision = correct/correct+wrong docs for test data. how to understand recall?
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Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved.
For a good enough accuracy metric in the machine learning model, you need a confusion matrix, recall, and precision. This is important because sometimes output may give you the wrong impression and to avoid it you need to see how well a model made its prediction.
Recall: The ability of a model to find all the relevant cases within a data set. Mathematically, we define recall as the number of true positives divided by the number of true positives plus the number of false negatives. Precision: The ability of a classification model to identify only the relevant data points.
Precision is calculated by dividing the true positives by anything that was predicted as a positive. Recall. Recall (or True Positive Rate) is calculated by dividing the true positives by anything that should have been predicted as positive. False Positive Rate.
Recall literally is how many of the true positives were recalled (found), i.e. how many of the correct hits were also found.
Precision (your formula is incorrect) is how many of the returned hits were true positive i.e. how many of the found were correct hits.
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