What is OOF approach in machine learning?

Question

I have seen in many kaggle notebooks people talk about oof approach when they do machine learning with K-Fold validation. What is oof and is it related to k-fold validation ? Also can you suggest some useful resources for it to get the concept in detail

Thanks for helping!

Answer 1

OOF simply stands for "Out-of-fold" and refers to a step in the learning process when using k-fold validation in which the predictions from each set of folds are grouped together into one group of 1000 predictions. These predictions are now "out-of-the-folds" and thus error can be calculated on these to get a good measure of how good your model is.

In terms of learning more about it, there's really not a ton more to it than that, and it certainly isn't its own technique to learning or anything. If you have a follow up question that is small, please leave a comment and I will try and update my answer to include this.

EDIT: While ambling around the inter-webs I stumbled upon this relatively similar question from Cross-Validated (with a slightly more detailed answer), perhaps it will add some intuition if you are still confused.

Answer 2

I found this article from machine learning mastery explaining out of the fold predictions quite in depth. Below an extract from the article explaining what out of fold (OOF) prediction is:

"An out-of-fold prediction is a prediction by the model during the k-fold cross-validation procedure. That is, out-of-fold predictions are those predictions made on the holdout datasets during the resampling procedure. If performed correctly, there will be one prediction for each example in the training dataset."