Optimal Classification Threshold Finder

1. 1Paste 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.
2. 2Or tap an example to load a good model, a weak model, an imbalanced set or the classic four-point example.
3. 3Read the AUC and average precision in the result card.
4. 4Switch between the ROC curve and the precision-recall curve.
5. 5Move the threshold slider, or press the best-threshold button, to see the confusion matrix and metrics at that cut-off.
6. 6Open the curve points table to see every threshold, false positive rate, true positive rate and precision.

• 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.

J = sensitivity + specificity - 1 = TPR - FPR

• The weak-model sample → gives best threshold 0.8, J 0.131

  A small J confirms that no threshold separates the classes well.

• Your scores and labels are processed by code running in your browser tab. They are never sent to Ulkaa or anyone else.

• 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.