Calculated Results
Results appear here after submission and stay above the calculator form.
Plotly Graph
Calculator Inputs
Choose a calculation mode. Use confusion matrix counts or sensitivity, specificity, and prevalence rates.
Example Data Table
This sample shows how positive predictive value is derived from observed classification counts.
| Condition / Test | Test Positive | Test Negative | Total |
|---|---|---|---|
| Condition Present | 85 (True Positive) | 20 (False Negative) | 105 |
| Condition Absent | 15 (False Positive) | 150 (True Negative) | 165 |
| Total | 100 | 170 | 270 |
Formula Used
Positive Predictive Value (PPV) measures the probability that a positive test result is truly positive.
Main formula:
PPV = TP / (TP + FP)
Rate-based form:
PPV = (Sensitivity × Prevalence) / [(Sensitivity × Prevalence) + ((1 − Specificity) × (1 − Prevalence))]
Related formulas included in this calculator:
NPV = TN / (TN + FN)Accuracy = (TP + TN) / (TP + TN + FP + FN)Precision = TP / (TP + FP)Recall = TP / (TP + FN)F1 Score = 2 × (Precision × Recall) / (Precision + Recall)False Positive Rate = FP / (FP + TN)
How to Use This Calculator
- Select Confusion Matrix Mode if you already know TP, FP, TN, and FN counts.
- Select Rates Mode if you know sensitivity, specificity, and prevalence.
- Enter the dataset label and preferred decimal precision.
- Press the calculation button to generate results above the form.
- Review PPV, NPV, accuracy, F1 score, and supporting metrics.
- Use the CSV or PDF buttons to export the current output.
- Inspect the chart to compare positive and negative classification behavior.
FAQs
1. What does positive predictive value mean?
Positive predictive value shows how often positive results are truly correct. It tells you the trustworthiness of a positive classification or screening outcome.
2. Why can PPV change even when sensitivity stays constant?
PPV depends strongly on prevalence and false positives. A rare condition can still produce a modest PPV even with a high-quality test.
3. Is PPV the same as precision?
Yes. In binary classification, PPV and precision use the same formula: true positives divided by all predicted positives.
4. When should I use confusion matrix mode?
Use confusion matrix mode when you already have observed counts from testing, auditing, machine learning evaluation, or a completed screening dataset.
5. When should I use rates mode?
Use rates mode during planning or forecasting. It estimates PPV from known sensitivity, specificity, prevalence, and a chosen population size.
6. What happens if TP and FP are both zero?
The PPV denominator becomes zero. In that case, PPV is undefined because the model or test produced no positive results.
7. Why does prevalence matter so much?
Prevalence affects the base rate of true cases. Lower prevalence usually reduces PPV because false positives occupy a larger share of positive results.
8. Can this calculator help with machine learning metrics?
Yes. It is useful for diagnostic studies, fraud detection, quality control, spam filtering, and classification models using binary outcome metrics.