About Average Precision Calculator
This average precision calculator ranks your cases by score and sums the precision at each recall step, weighted by the size of the step. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.
Average precision rewards a model that puts the real positives near the top of the ranking. Unlike the trapezoid AUC it does not interpolate between points, so a positive found early contributes its full precision. It is the definition used by scikit-learn's average_precision_score.
The example is a weak model whose scores only loosely follow the labels, so its average precision of 0.56 is only a little above the share of positives, which is 0.45 here.
How to use Average Precision Calculator
- Paste your data as score,label lines. A label is 1 for the positive class and 0 for the negative class; true and false also work.
- Or tap an example to load a good model, a weak model, an imbalanced set or the classic four-point example.
- Read the AUC and average precision in the result card.
- Switch between the ROC curve and the precision-recall curve.
- Move the threshold slider, or press the best-threshold button, to see the confusion matrix and metrics at that cut-off.
- Open the curve points table to see every threshold, false positive rate, true positive rate and precision.
Features
- ROC curve with the chance diagonal, shaded area and a marker for your chosen threshold.
- AUC by the trapezoid rule, with ties between a positive and a negative counted as half.
- Precision-recall curve with the no-skill baseline, and average precision.
- A threshold slider with the live confusion matrix, accuracy, precision, recall, false positive rate, F1 and MCC.
- The best threshold by Youden's J statistic in one tap.
- Up to 20,000 rows, with a table of the curve points.
- A plain-English reading of what the AUC means for your model.
Average precision formula
R_n and P_n are the recall and precision at the nth threshold.
AP = sum over n of (R_n - R_n-1) x P_nWorked examples
The weak-model sample, 20 cases, 9 positive gives average precision 0.562, AUC 0.525
Compare with the base rate of 0.45, which is what a random ranking would give.
Privacy: how it works
- Your scores and labels are processed by code running in your browser tab. They are never sent to Ulkaa or anyone else.
Tips
- Scores need not be probabilities. Any number where higher means more likely positive works, because only the ranking matters.
- The AUC depends only on the order of the scores. Multiplying all scores by a constant changes nothing.
- Tied scores are handled as one step. A tie between a positive and a negative cuts a diagonal and counts half.
- Both classes must appear in the data, or the rates are undefined and the tool asks for more.
Frequently asked questions
Is it really free?
Yes. There is no price, no trial and no credit card. Ulkaa Tool does the work with your own device, so there is no server bill to pass on to you.
Do I need an account?
No. There is no sign-up and no login. Open the page and use it. An Ulkaa account is not needed for anything on this tool.
Does it run in my browser?
Yes. Your scores and labels are processed by code running in your browser tab, on your own device.
Is my data uploaded?
No. Your scores and labels are never sent to Ulkaa or to anyone else. You can check this yourself by watching the Network tab in your browser developer tools while you use the tool.
What is the difference between AP and AUC?
AUC is the area under the ROC curve and ignores class balance. Average precision is the area under the precision-recall curve and is sensitive to how rare the positives are.
Is average precision the same as mAP?
No. Mean average precision (mAP) is the average of AP over several classes or queries. This tool computes the AP of one ranked list.
More ROC Curve and AUC Calculator pages
- ROC Curve and AUC CalculatorPaste model scores and true labels to draw the ROC curve, find the AUC and explore thresholds.
- ROC Curve CalculatorPaste scores and labels to plot the ROC curve and move along it with a threshold slider.
- AUC CalculatorPaste scores and labels and get the area under the ROC curve.
- Precision-Recall Curve CalculatorSee precision against recall for every threshold, and compare it with the no-skill line.
- Optimal Classification Threshold FinderFind the cut-off that best balances true positives and false positives.
- ROC AUC for Imbalanced DataCheck what a ROC AUC means when only a few cases are positive.