Calculate multilayer perceptron metrics from confusion matrix inputs. Check precision, recall, specificity, MCC, and F1. Download clean reports and evaluate model behavior with confidence.
| Model Run | TP | TN | FP | FN | Accuracy | Precision | Recall | F1 | MCC |
|---|---|---|---|---|---|---|---|---|---|
| Validation Fold A | 92 | 138 | 18 | 12 | 88.4615% | 0.8364 | 0.8846 | 0.8598 | 0.7628 |
Multilayer perceptron classifiers are widely used for pattern recognition, credit scoring, medical screening, churn prediction, and image labeling. Good model evaluation is essential. Raw accuracy alone can hide important weaknesses. A model may look strong while missing many positive cases or generating too many false alerts.
This calculator helps you measure confusion matrix based performance in one place. You enter true positives, true negatives, false positives, and false negatives. The tool then computes accuracy, error rate, precision, recall, specificity, negative predictive value, F1 score, false positive rate, false negative rate, balanced accuracy, Jaccard score, Youden’s J, prevalence, and Matthews correlation coefficient.
These metrics describe different model behaviors. Precision shows how many predicted positives were correct. Recall shows how many actual positives were found. Specificity tracks correct negative recognition. Balanced accuracy is useful when classes are uneven. MCC is valuable when you need one stable summary metric across imbalanced datasets.
Use the output to compare different hidden layer settings, activation functions, solvers, and probability thresholds. If recall is low, your classifier may miss important positive events. If precision is low, it may create expensive false alarms. If training accuracy is much higher than validation accuracy, the network may be overfitting.
Review metrics together instead of relying on one number. Start with balanced accuracy and MCC for a broader view. Then inspect precision, recall, specificity, and F1 for task level tradeoffs. For regulated or high cost decisions, also document threshold choice, class balance, and validation method. Exporting your summary makes benchmarking and reporting easier across repeated experiments.
A high accuracy value with poor recall can be risky in fraud, disease, and defect detection. A high recall value with poor precision may overload review teams. Watch false positive rate when user trust matters. Watch false negative rate when missing a positive case is costly. NPV helps when negative predictions must be dependable. Jaccard score is useful for overlap style evaluation. Together, these measures give a stronger and more realistic picture of multilayer perceptron classifier quality. Use this calculator before deployment, retraining, reporting, or threshold tuning.
You need true positives, true negatives, false positives, and false negatives. These four values build the confusion matrix and drive all core performance metrics.
MCC summarizes confusion matrix quality in one value. It stays informative when classes are imbalanced, which makes it more reliable than accuracy alone.
Focus on recall when missing a positive case is costly. Examples include fraud detection, medical screening, safety alerts, and defect identification.
Precision matters when false alarms are expensive. It helps when every positive prediction triggers manual review, customer outreach, or operational cost.
Balanced accuracy averages recall and specificity. It gives a fairer view when one class is much larger than the other.
The generalization gap is training accuracy minus validation accuracy. A large positive gap often suggests overfitting and weaker real world performance.
Yes. They are optional inputs. Add them when you already computed threshold independent ranking metrics elsewhere and want one report.
Exports help with experiment tracking, audits, stakeholder reporting, and comparison across model runs, folds, datasets, and threshold settings.
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.