Model Performance Calculator

Complete ML model evaluation. Classification metrics, regression analysis, cross-validation, ROC/AUC curves, and model comparison.

Progress0%

Evaluation Type

True Positives (TP)

True Negatives (TN)

How to Use This Calculator

Enter actual vs. predicted values for your test set.
Select the task type (classification or regression) to compute relevant metrics.
Review classification metrics (accuracy, F1, AUC) or regression metrics (RMSE, MAE, R²).
Check for systematic bias by inspecting residuals or class-level performance.
Use the per-class metrics breakdown to identify which classes the model underperforms on.

What each input means

F1 = 2×(P×R)/(P+R) | R² = 1 - SS_res/SS_tot | AUC = ∫TPR d(FPR)

Identify Input Parameters
4 parameters
Evaluation Type = 0, True Positives (TP) = 85, True Negatives (TN) = 90, False Positives (FP) = 10 = 22 input(s) provided
Calculate Accuracy
Accuracy = model1Accuracy * 100
0.875 = 0.875%
Calculate F1 Score
F1 Score
0.8717948717948718 = 0.8717948717948718%
Calculate Precision
Precision
0.8947368421052632 = 0.8947368421052632%
Calculate Recall
Recall
0.85 = 0.85%

Engine last updated August 5, 2026. Checked against 5 independently-derived tests — how we verify calculators.

The questions that sit next to this one — chosen by subject, including calculators filed under a different category.

Complete ML algorithm analysis. KNN, decision trees, random forests, SVMs, clustering, and gradient boosting parameters and complexity.

Complete neural network training analysis. Architecture design, learning rates, batch sizes, regularization, and activation functions.

Calculate accuracy, precision, recall, F1 score, MCC, AUC, and other classification performance metrics.

Analyze classification model performance from confusion matrix values. Calculate precision, recall, F1 score, MCC, accuracy, specificity, and more.

Calculate total experiments, time, and GPU cost for hyperparameter grid search with cross-validation.