Splitting dataframe into multiple ones according to proportion of samplesize and labels
When you have an imbalanced dataset, you can use the parameter 'stratify' in the train_test_split(). This will make the dataset be split into training and test sets in such a way that the ratio of the class labels in the variable specified is constant i.e. both train and test set will have the same ratio of class labels.
I want to create my training, validation and testset from one dataframe with
a proportion of 6:2:2.
But additionally within each set, I'd like to have a proportion
of 6:4 between the 2 labels. Among the original dataframe this 6:4 proportion is
not given, one label is massively overrepresented.
Maybe I should adjust that in advance?
I think sklearns train_test_split() might be an option but to be honest
its documentation did not make me any wiser...
Are there any best practices for this kind of problem?