Productive ToolboxModel Accuracy Calculator
Calculate machine learning model accuracy instantly. Compare actual vs predicted labels, evaluate AI performance, upload CSV data, and get instant results online for free.
Model Accuracy Calculator
Paste actual and predicted labels to instantly compute classification accuracy. Supports binary, multi-class, and text labels. Upload CSV for batch evaluation. All computation runs locally.
Labels
Comma, newline, or tab separated · bare 0s/1s also work
Ctrl+Enter to recalculate
Example Datasets
Accuracy Score
Enter actual and predicted labels to see accuracy results and the comparison table.
Try an example above or paste comma-separated values like 1,1,0,1,0
How the Model Accuracy Calculator Works
Model accuracy is the simplest and most widely used evaluation metric for classification tasks in machine learning. It measures the fraction of predictions that exactly match the actual (true) labels. This tool calculates accuracy instantly in your browser — no data is uploaded anywhere.
Paste your actual labels and predicted labels as comma-separated values, or upload a two-column CSV file with actual,predicted columns. The tool compares them element-by-element and shows the accuracy score, per-class breakdown, and a side-by-side comparison table.
The Formula
Accuracy = (Correct Predictions ÷ Total Predictions) × 100 Example: Actual: [1, 1, 0, 1, 0] Predicted: [1, 0, 0, 1, 0] Matches: ✓ ✗ ✓ ✓ ✓ → 4 correct out of 5 Accuracy = (4 ÷ 5) × 100 = 80.00%
Accuracy Performance Reference
| Accuracy | Rating | Context |
|---|---|---|
| ≥ 90% | Excellent | Production-grade — suitable for most real-world applications |
| 75–89% | Good | Useful model — may need fine-tuning for critical tasks |
| 50–74% | Moderate | Better than random for binary, but likely needs improvement |
| < 50% | Poor | Worse than random guessing for binary classification |
Supported Input Formats
| Format | Example | Parsed as |
|---|---|---|
| Comma-separated | 1,0,1,1,0 | [1, 0, 1, 1, 0] |
| Newline-separated | 1\n0\n1\n1 | [1, 0, 1, 1] |
| Tab-separated | 1\t0\t1\t1 | [1, 0, 1, 1] |
| Bare binary run | 10110 | [1, 0, 1, 1, 0] |
| Text labels | cat,dog,cat,bird | [cat, dog, cat, bird] |
| CSV upload | actual,predicted\n1,1\n0,1 | Two columns parsed automatically |
When Accuracy Is Not Enough
Accuracy is intuitive but can be misleading on imbalanced datasets. If 95% of your data belongs to one class, a model that always predicts that class achieves 95% accuracy while being completely useless. In such cases, consider:
- Precision: Of all positive predictions, how many were actually positive?
- Recall: Of all actual positives, how many were correctly identified?
- F1 Score: Harmonic mean of precision and recall — good for imbalanced data.
- ROC-AUC: Area under the ROC curve — model-wide performance across thresholds.
- Confusion Matrix: Full breakdown of TP, TN, FP, FN across all classes.
Frequently Asked Questions
What is model accuracy in machine learning?
Model accuracy is the percentage of predictions that match the true labels in a test dataset. It is calculated as (correct predictions ÷ total predictions) × 100. It is the most commonly reported metric for classification tasks.
What is a good accuracy for a machine learning model?
It depends heavily on the problem. For balanced binary classification, 90%+ is typically excellent. For highly imbalanced datasets (e.g. fraud detection where 0.1% are fraudulent), even 99.9% accuracy can be meaningless — the model might just be predicting the majority class.
How do I calculate accuracy for multi-class classification?
The formula is identical: count how many predictions exactly match the actual label, divide by total, and multiply by 100. This tool automatically handles multi-class labels — just paste your actual and predicted lists with matching length.
What is the difference between training accuracy and test accuracy?
Training accuracy is measured on the data used to train the model. Test accuracy is measured on held-out data the model has never seen. Test accuracy is the meaningful metric — high training accuracy with low test accuracy indicates overfitting.
Does this tool support uploading CSV files?
Yes. Switch to CSV mode and upload a .csv or .txt file with two columns named 'actual' and 'predicted'. The tool parses the file locally in your browser — no data is uploaded to any server.
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