[sangnoir]

> after all, why would you ship a worse model to prod?

...because you need a control to evaluate how well your product is doing? I know it's a young field, but boy, do some folk love removing the "science" from "data science"

[baobabKoodaa]

You can evaluate a version of the model that has been trained on one set of data, and ship to production a different model that has been trained on the complete set of data. In many cases one can reasonably infer that the model which has seen all of the data will be better than the model which has seen only some of the data.

I'm not claiming that's what happened here, nor am I interested in nitpicking "what counts as 'science'". I'm just saying this is a reasonable thing to do.

[mafuy]

This is possible if you use e.g. train 1000 models on different subsets of data and verify that each and every one of them is performing well. In that case, you can reasonably infer that another model trained on all data would work well, too.

But this is, of course, 1000 times more expensive to do. And if you only train 100, or 10, or 1 model, then the deduction becomes increasingly unstable.

So from a practical point of view, it's probably not feasible, because you would put those resources into something else instead that has more ROI.

[baobabKoodaa]

I have personally never seen a situation where more training data (of similar quality) causes the model to perform worse. Have you seen such a situation? Please provide example.

Your suggestion of running 1000 training runs with different subsets of data sounds excessive and unnecessary to me.

[nightski]

You have to know when to stop training. How are you going to do that without a test set? How do you know when you have achieved generalization without over-fitting?

[wedesoft]

Early stopping is just one way of regularization. You can use L2 or dropout and then you can train until your model converges.

[baobabKoodaa]

Usually I develop models with a train/validation/test split, where I'm measuring results on the validation set to decide the appropriate number of epochs to use. Then I burn the test set to evaluate performance. Then I train from scratch on the entire dataset (no split) and I use the same number of epochs to train here. Is this number of epochs optimal when the dataset is different? Of course not. But when you use regularization and other methods to combat overfitting appropriately, your training is not going to be overly sensitive to changes in epoch number anyway.

[peterlk]

In the case of fine tuning, you can end up with catastrophic forgetting. Architecture can influence how data scales, and adding data doesn’t always improve performance

[Naracion]

>infer that the model which has seen all of the data will be better than the model which has seen only some of the data.

It really depends upon the data. A smaller set of data that mostly consists of "truth" might be better than a larger dataset that also has many "lies".

Perhaps what you mean is that the model might be more representative, rather than _better_.

[janalsncm]

There are offline metrics and online metrics. Offline metrics might be something like AUROC on a test set. Once you’ve pushed the model online, you can check the online metrics. Ultimately the online metrics are more important, that’s the whole reason the model exists in the first place.

Your control in an online environment is the current baseline. You don’t need to save the test set anymore, you can push it online and test it directly.