When should I use compression or regularization in machine learning?

What’s the difference between compression and regularization in machine learning?

Compression reduces the size of the model, while regularization prevents overfitting by adding a penalty to the loss function.

When should I choose one over the other?

Use regularization when your model is overfitting, and compression when you need to deploy a smaller model without losing much accuracy.

Can you give examples of techniques for each?

Sure! For regularization, techniques include L1 and L2 regularization. For compression, you might use techniques like pruning or quantization.

How do I know if my model is overfitting?

You can check for overfitting by comparing training and validation performance; if training accuracy is high but validation accuracy is low, it’s likely overfitting.