Do I remove outliers within training set or duplicate of original? I want to predict on a test set.
I have created a binary logistic regression using my current training set and have predicted on the test set. The dataset I used to split has 299 observations.
What if I want to remove outliers? Do I only remove them within the training set or from the entire dataset and split it once again?
If it's the latter, isn't there a chance that I would end up predicting on the observations that were previously within the training set?
Would I get around this by setting a seed in the programming software set.seed(123)?

We take the data and do our train/test split. We define what an outlier is using our training set only. Using this definition we remove outliers from both sets. We continue as normal, i.e. yes, we can remove outliers from our test set, if we think based on our training set that they are corrupted data, machine errors, etc.
If use "all" the data together to define our outlier, we have data leakage. Information that we won't have at deployment/testing time normally is used to adjust the training set, which leads to overfitting and poor generalisable performance.
And to state the obvious: An outlier is an outlier irrespective of the seed choice. Changing the random seed does not "fix" any sample quality problems we might have.
