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

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

Accuracy measures the proportion of correct predictions, while loss quantifies how far off predictions are from actual values.

Can you explain how they affect model performance?

Sure! High accuracy means the model is making correct predictions, but a low loss indicates it's learning well. They should ideally improve together.

What happens if accuracy is high but loss is also high?

That could indicate overfitting, where the model performs well on training data but poorly on unseen data.

How can I improve both accuracy and loss in my model?

You can try techniques like regularization, tuning hyperparameters, or using more data to improve both metrics.