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Calcimator

ROC Curve Calculator

Calculate sensitivity, specificity, PPV, NPV, accuracy, F1 score, and approximate AUC from a confusion matrix (TP, FP, TN, FN).

Inputs

Results

Sensitivity (Recall / TPR)

0.85

Specificity (TNR)

0.95

F1 Score

0.87

Positive Predictive Value (Precision)0.89
Negative Predictive Value0.93
Accuracy0.92
AUC (single-point approx)0.9
Youden's J Statistic0.8
Positive Likelihood Ratio17
Negative Likelihood Ratio0.16
How to Use This Calculator
  1. Enter True Positives (TP), False Positives (FP), and True Negatives (TN).
  2. Set False Negatives (FN).
  3. Review Sensitivity (Recall / TPR), Specificity (TNR), and F1 Score.
  4. Use Positive Predictive Value (Precision) and Negative Predictive Value to inform your decision.
  5. Use the chart to visualize the results and explore different scenarios by adjusting inputs.

What each input means

True Positives (TP)
Number of correctly identified positive cases.
False Positives (FP)
Number of negative cases incorrectly classified as positive (Type I error).
True Negatives (TN)
Number of correctly identified negative cases.
False Negatives (FN)
Number of positive cases incorrectly classified as negative (Type II error).

How this is calculated

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    True Positives (TP) = 85, False Positives (FP) = 10, True Negatives (TN) = 190, False Negatives (FN) = 15 = 4 input(s) provided
  2. Calculate Sensitivity
    Sensitivity
    0.85 = 0.85
  3. Calculate Specificity
    Specificity
    0.95 = 0.95
  4. Calculate F1 Score
    F1 Score = f1
    0.8718 = 0.8718
  5. Calculate Positive Predictive Value
    Positive Predictive Value
    0.8947 = 0.8947
  6. Calculate Negative Predictive Value
    Negative Predictive Value
    0.9268 = 0.9268

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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