Калькулятор матрицы ошибок
Вычислите Точность, Полноту, Recall, F1, Специфичность, NPV, FPR, FNR и Коэффициент корреляции Мэтьюса из подсчётов TP/TN/FP/FN бинарного классификатора, с формулой и оценкой качества для каждой метрики.
Ввод
Случаи, правильно предсказанные как положительные.
Фактические положительные результаты, пропущенные моделью (предсказаны как отрицательные).
Фактические отрицательные результаты, отмеченные моделью как положительные.
Случаи, правильно предсказанные как отрицательные.
Вывод
Your results will appear here.
| Predicted Positive | Predicted Negative | |
|---|---|---|
| No data yet | ||
| Metric | Formula | Value | Rating |
|---|---|---|---|
| No data yet | |||
Другие способы использовать этот инструмент
REST API
curl -X POST https://api.iotools.cloud/v1/tool/confusion-matrix-calculator \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"truePositive": "50",
"falseNegative": "10",
"falsePositive": "5",
"trueNegative": "100"
}'Подставьте свой собственный ключ из аккаунта. Поля инструмента — это тело запроса, без обёртки.
Спросите у ИИ-агента
Use the IOTools `confusion-matrix-calculator` tool (Confusion Matrix Calculator) on this input:
YOUR_INPUT_HEREВставьте это любому агенту, подключённому к MCP-серверу IOTools, и добавьте свой ввод.
Виджет для встраивания
<iframe
src="https://iotools.cloud/embed/confusion-matrix-calculator/"
width="100%" height="520" frameborder="0" scrolling="no" loading="lazy"
title="Калькулятор матрицы ошибок — iotools.cloud"
sandbox="allow-scripts allow-forms allow-same-origin allow-downloads allow-popups allow-popups-to-escape-sandbox"
allow="clipboard-write"
style="width:100%;border:1px solid #e5e7eb;border-radius:12px;overflow:hidden"></iframe>
<script src="https://iotools.cloud/embed.js" async></script>Вставьте это на свою страницу — бесплатно, без ключа, нужна лишь обратная ссылка.
| Стоимость вызова API/MCP | От 5 кредитов |
|---|---|
| Нужно больше кредитов? | Посмотреть тарифы |
Также доступно через
Руководства
What does this calculator do?
Enter the four counts from a binary classifier's confusion matrix — True Positive, False Negative, False Positive, and True Negative — and get every standard evaluation metric computed instantly: Accuracy, Precision, Recall, Specificity, F1 Score, Matthews Correlation Coefficient (MCC), Negative Predictive Value, False Positive Rate, False Negative Rate, and a Balanced Accuracy estimate. Each metric shows its formula and a quality rating (Excellent / Good / Fair / Poor), so you don't have to remember which formula is which or re-derive them by hand.
How to use it
- Enter True Positive (TP) — cases correctly predicted positive.
- Enter False Negative (FN) — actual positives the model missed.
- Enter False Positive (FP) — actual negatives the model flagged as positive.
- Enter True Negative (TN) — cases correctly predicted negative.
- Read the results: an overall quality gauge, the matrix laid out as a table, and every metric with its formula and rating.
Why use MCC instead of just Accuracy?
Accuracy can be misleading on imbalanced datasets — a classifier that always predicts "negative" on a dataset that's 99% negative scores 99% accuracy while catching zero positives. Matthews Correlation Coefficient uses all four confusion-matrix counts in one balanced measure, ranging from -1 (total disagreement) to +1 (perfect prediction), with 0 meaning no better than random guessing — which is why it's considered the more reliable single number for imbalanced classes.
What's the difference between Precision and Recall?
Precision answers "of everything I predicted positive, how much was actually positive?" (TP / (TP+FP)) — it penalizes false alarms. Recall (Sensitivity) answers "of everything that was actually positive, how much did I catch?" (TP / (TP+FN)) — it penalizes misses. The two trade off against each other, which is why F1 Score (their harmonic mean) is often reported as a single balance point between them.
Is the "AUC estimate" a real AUC?
Not exactly — a true ROC AUC needs classifier scores across many decision thresholds, which a single confusion matrix (one threshold) can't provide. The "AUC estimate" shown here is Balanced Accuracy — the average of Recall and Specificity — a common single-threshold approximation, not a substitute for computing a real ROC curve if you have per-sample scores.
Privacy
All calculations run locally in your browser — your counts are never sent to a server.
For evaluating whether a difference between two rates is statistically significant (e.g. comparing two models' conversion or error rates), see the A/B Test Significance Calculator. To plan how much data you'd need to detect a given effect size before collecting it, see the A/B Test Sample Size Calculator.