# Training data size requirements for imbalanced class classification

Our team is planning to start data collection for an imbalanced binary classification task. One in 30 cases is positive class and the rest 29 of the 30 cases are negative class. We have around 20 features (both categorical and numeric). We are initially planning to collect around 60,000 data points so that we will end up with 2000 positive classes.

Can you please comment if the size of the training data looks alright for the imbalanced classification task?

I understand that the numbers also depend on the finer details like the values of the feature matrix and the type of the classifier. But if we want to have an approximate ballpark number for the data set size, what number would you recommend if we are aiming for an AUC of 0.9? And are there any guidelines for the size of the dataset?

Many thanks

First, note than many models in sklearns have the option class_weight='balanced' that allows the model dealing with unbalanced classes by itself, so wathever your unbalance ratio is, the result using this option might be better than if you remove it and try to manually balance data.