How to Check the Accuracy of Your Machine Learning Model in 2025

What you’ll learn:

• What accuracy in ML means, and how you can use it to evaluate classification models.
• What the differences between evaluation metrics like Accuracy, Precision, and Recall are, and when to use them.
• How precision serves your purpose better than accuracy when you care more about false positives.

FAQ’s

Why is accuracy important in machine learning models?

If you’re curious about what accuracy is in machine learning, it is a metric that gives you your model’s overall performance by measuring the number of correct predictions out of the total number of predictions it makes. For example, if your model makes 10 predictions and 8 of them are correct, its accuracy is 80%. Accuracy acts as a good baseline metric.

What is the difference between accuracy and precision in machine learning?

In machine learning model evaluation, a hard line exists between accuracy and precision. Accuracy measures the percentage of correct predictions out of the overall predictions. On the other hand, precision measures the percentage of correct positive predictions among all positive predictions.

How are accuracy, precision, and recall related in machine learning?

Accuracy, precision, and recall are evaluation metrics used to test the performance of a classification model in machine learning. Accuracy gives you the percentage of correct predictions out of all predictions. Precision measures the percentage of true positives over predicted positives, and recall measures the percentage of true positives over actual positives.

When should I use precision instead of accuracy for my model?

We often debate the trade-off between accuracy and precision in machine learning. Use precision when your data is imbalanced and you cannot afford false positives. In such cases, accuracy appears high even when there are missing positive predictions. However, precision focuses on positive predictions, which makes it reliable when dealing with imbalanced datasets.