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What does average precision tell you?
Average precision is a measure that combines recall and precision for ranked retrieval results. For one information need, the average precision is the mean of the precision scores after each relevant document is retrieved.
What is the precision of the classifier?
Precision quantifies the number of positive class predictions that actually belong to the positive class. Recall quantifies the number of positive class predictions made out of all positive examples in the dataset. F-Measure provides a single score that balances both the concerns of precision and recall in one number.
What is average precision and average recall?
Recall measures how well you find all the positives. For example, we can find 80% of the possible positive cases in our top K predictions. The general definition for the Average Precision (AP) is finding the area under the precision-recall curve above. mAP (mean average precision) is the average of AP.
When do you need a better classifier for machine learning?
Generally, if you want higher precision you need to restrict the positive predictions to those with highest certainty in your model, which means predicting fewer positives overall (which, in turn, usually results in lower recall). If you want to maintain the same level of recall while improving precision, you will need a better classifier.
How is precision calculated in multi class classification?
Precision for Multi-Class Classification. Precision is not limited to binary classification problems. In an imbalanced classification problem with more than two classes, precision is calculated as the sum of true positives across all classes divided by the sum of true positives and false positives across all classes.
When do you need a better classifier for recall?
If you want to maintain the same level of recall while improving precision, you will need a better classifier. (for problems like these, always have the two by two contingency table in mind; see wikipedia’s Recall/Precision or Sensitivity/Specificity for details)
How is precision calculated in imbalanced classification problem?
In an imbalanced classification problem with two classes, precision is calculated as the number of true positives divided by the total number of true positives and false positives. The result is a value between 0.0 for no precision and 1.0 for full or perfect precision. Let’s make this calculation concrete with some examples.