Overfitting is the state where an estimator has begun to learn the training set so well that it has started to model the noise in the training samples (besides all useful relationships). For …

Mar 3, 2019 · Overfitting is when a model estimates the variable you are modeling really well on the original data, but it does not estimate well on new data set (hold out, cross validation, …

So, overfitting in my world is treating random deviations as systematic. Overfitting model is worse than non overfitting model ceteris baribus. However, you can certainly construct an example …

Jan 4, 2020 · $\begingroup$ @dmartin: Thank you for you upvote but apologies as I somewhat disagree with the point you make. . Unless we are looking at a severely imbalanced problem a …

Oct 25, 2019 · The optimization set helps against overfitting, but it is not a magic bullet: it can only help so much. So it does help guarding against slight overfitting, but you may look at severely …

Aug 15, 2014 · $\begingroup$ Overfitting is when you have your train << oob/cv score. This is often the case for the RFs I have used. People keep repeating that Brieman thinks there is no …

Mar 28, 2016 · In convolutional neural network how can I identify overfitting? Comparing the performance on training (e.g., accuracy) vs. the performance on testing or validation is the …

Jul 19, 2020 · As I understand it, overfitting is the result of model selection based on training and testing using the same data, where you have a flexible fitting mechanism: you fit your sample …

Jan 27, 2022 · What you need is to compare the performance on the training test to performance on test set, that could give you some idea about potential overfitting. As about general model …

On the other hand, if you have a huge model and the accuracies are similar, you still might have over fitted but also failed to exploit that overfitting. Example of such a case is a large random …