How do I evaluate the performance of my machine learning model?

How do I evaluate the performance of my machine learning model?

You can evaluate your model's performance using metrics like accuracy, precision, recall, and F1 score. What type of model are you using?

I'm using a classification model for image recognition.

For classification models, accuracy is a common metric, but you should also consider precision and recall, especially if your classes are imbalanced.

What is the difference between precision and recall?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances. Both are important for understanding model performance.

How can I visualize these metrics?

You can use confusion matrices, ROC curves, and precision-recall curves to visualize these metrics effectively.