Measure classifier quality with balanced statistical performance checks. Compare MCC and accuracy using confusion matrix outcomes. Reveal hidden model weakness across skewed class distributions.
| Case | TP | TN | FP | FN | Accuracy | MCC | Insight |
|---|---|---|---|---|---|---|---|
| Balanced strong model | 45 | 45 | 5 | 5 | 0.900000 | 0.800000 | Both metrics confirm strong performance. |
| Imbalanced misleading case | 5 | 90 | 0 | 5 | 0.950000 | 0.688247 | Accuracy looks higher than true balance. |
| Weak classifier | 20 | 60 | 20 | 20 | 0.666667 | 0.288675 | MCC exposes limited predictive reliability. |
Accuracy = (TP + TN) / (TP + TN + FP + FN)
Matthews Correlation Coefficient = (TP × TN − FP × FN) / √((TP + FP)(TP + FN)(TN + FP)(TN + FN))
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
Specificity = TN / (TN + FP)
Balanced Accuracy = (Recall + Specificity) / 2
MCC ranges from -1 to +1. A value near +1 shows strong agreement. A value near 0 suggests random behavior. A negative value indicates disagreement between predictions and actual labels.
Accuracy is easy to understand. It tells you how many predictions were correct. That sounds useful, but it can hide major problems. This often happens with imbalanced datasets. A model may predict the majority class well and still miss important minority cases.
Matthews correlation coefficient gives a fuller view. It uses all four confusion matrix values. That means true positives, true negatives, false positives, and false negatives all matter. Because of this, MCC is often better for binary classification review.
Imagine a rare disease test. If most patients are healthy, a lazy model can predict “healthy” almost every time. Accuracy may still look high. Yet the model may fail the real task. MCC helps uncover that weakness because it punishes one-sided performance.
Use accuracy for a quick summary. Use MCC when class sizes are uneven or costs differ. Together, these metrics show both overall correctness and balanced prediction quality. This makes model evaluation more trustworthy.
This calculator compares MCC and accuracy from confusion matrix counts. It also returns precision, recall, specificity, F1 score, error rate, prevalence, and balanced accuracy. These extra measures help explain why two models with similar accuracy can behave very differently.
Data scientists, students, researchers, and quality analysts can use this tool to inspect classifier outcomes. It is useful during threshold tuning, model selection, validation review, and report creation. The CSV export supports further analysis. The PDF option helps share results quickly.
If you only track accuracy, you may overrate weak models. If you also track MCC, you get a more balanced statistical picture. That leads to better decisions, cleaner evaluations, and stronger machine learning or statistical reporting.
MCC is a binary classification metric that uses all confusion matrix values. It measures balanced prediction quality and works well when classes are uneven.
Accuracy can stay high when a model predicts the majority class only. MCC checks both positive and negative performance, so it reveals imbalance issues more clearly.
Values near 1 are excellent. Values around 0 suggest random prediction. Negative values show disagreement between predicted and actual classes.
Yes. Two models may share identical accuracy while handling minority cases very differently. MCC highlights that difference through balanced evaluation.
You need four counts: true positives, true negatives, false positives, and false negatives. These form the confusion matrix.
MCC becomes undefined when a denominator term equals zero. This usually happens when one prediction or one actual class contains no observations.
Yes. It is useful for binary classifiers in machine learning, medical testing, fraud detection, quality control, and many other statistical applications.
No. Use MCC with accuracy, precision, recall, specificity, and F1 score. Multiple metrics give a more complete model assessment.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.