混淆矩阵计算器
从二分类器的TP/TN/FP/FN计数中计算准确率、精确率、召回率、F1、特异性、NPV、FPR、FNR和马修斯相关系数，每个指标都显示其公式和质量等级。
输入
正确预测为正例的案例。
模型遗漏的实际正例（预测为负例）。
被模型标记为正例的实际负例。
正确预测为负例的案例。
输出
Your results will appear here.
| Predicted Positive | Predicted Negative | |
|---|---|---|
| No data yet | ||
| Metric | Formula | Value | Rating |
|---|---|---|---|
| No data yet | |||
使用此工具的更多方式
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"
}'替换成您账户中的密钥。工具的字段即为请求体——没有额外包装。
让 AI 代理执行
Use the IOTools `confusion-matrix-calculator` tool (Confusion Matrix Calculator) on this input:
YOUR_INPUT_HERE将此粘贴给任何已连接 IOTools MCP 服务器的代理,再加上您的输入内容。
嵌入式小组件
<iframe
src="https://iotools.cloud/embed/confusion-matrix-calculator/"
width="100%" height="520" frameborder="0" scrolling="no" loading="lazy"
title="混淆矩阵计算器 — 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>把它放到您自己的页面上——免费,无需密钥,只需保留一个反向链接。
| 每次 API/MCP 调用费用 | 5 积分起 |
|---|---|
| 需要更多积分? | 查看定价 |
也可通过
使用指南
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.