Question 1

What is model accuracy in machine learning?

Accepted Answer

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.

Question 2

What is a good accuracy for a machine learning model?

Accepted Answer

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.

Question 3

How do I calculate accuracy for multi-class classification?

Accepted Answer

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.

Question 4

What is the difference between training accuracy and test accuracy?

Accepted Answer

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.

Question 5

Does this tool support uploading CSV files?

Accepted Answer

Yes. Switch to CSV mode and upload a .csv or .txt file with two columns named 'actual' and 'predicted'. We do not collect or store your files.

Model Accuracy Calculator