I am writing my master thesis on imbalanced dataset impact on active learning methods. I have stumbled upon a problem in bootstrapping. When there is few minority class samples, sometimes models are bootstrapped only majority class samples and it results with:
ValueError: The number of classes has to be greater than one; got 1 class
Would it be reasonable to implement stratified bootstrapping? I will make a pull request soon with the proposition.
I am writing my master thesis on imbalanced dataset impact on active learning methods. I have stumbled upon a problem in bootstrapping. When there is few minority class samples, sometimes models are bootstrapped only majority class samples and it results with:
ValueError: The number of classes has to be greater than one; got 1 classWould it be reasonable to implement stratified bootstrapping? I will make a pull request soon with the proposition.