TATITIC CALCULATOR
Confusion Matrix
A precise tool.
What is the Confusion Matrix & How does it work?
A confusion matrix is a 2×2 table that summarises the performance of a binary classifier by counting the four possible outcomes: true positives (TP), false positives (FP), false negatives (FN) and true negatives (TN). These raw counts form the foundation for every downstream metric used in model evaluation. From the matrix we can derive accuracy, precision, recall (sensitivity) and the F1‑score, each highlighting a different aspect of predictive quality. Accuracy measures overall correctness, while precision focuses on the relevance of positive predictions and recall captures the ability to find all actual positives. Because the metrics are inter‑dependent, a single number rarely tells the whole story. Practitioners often balance precision against recall using the harmonic mean (F1‑score) to select models that meet domain‑specific cost constraints.
\text{Accuracy} = \frac{TP + TN}{TP + FP + FN + TN}
Acc = proportion of correct predictions
Parameters
Result
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Frequently Asked Questions
What is a confusion matrix?
A confusion matrix is a table that summarizes the performance of a binary classifier by showing true positives, false positives, false negatives, and true negatives.
How do I interpret the results from this calculator?
The calculator provides raw counts for TP, FP, FN, TN and derived metrics like accuracy, precision, recall, and F1-score to evaluate your model’s performance.
What is the difference between precision and recall?
Precision measures the accuracy of positive predictions, while recall measures the ability of a classifier to find all relevant instances.
Can this calculator handle multi-class classification?
No, this confusion matrix calculator is designed for binary classification only. For multi-class models, consider using a different tool or method.
What does the F1-score represent?
The F1-score is the harmonic mean of precision and recall, providing a balance between them to evaluate model performance.
How do I input data into this calculator?
Enter the counts for true positives, false positives, false negatives, and true negatives in their respective fields, then click calculate.
Why is accuracy not always a good metric?
Accuracy can be misleading if the class distribution is imbalanced. It’s better to use precision, recall, F1-score, or other metrics that consider both true and false predictions.
Results are for informational purposes only and do not constitute professional advice.