I am working with an imbalanced dataset where I have a class variable of 2 different values: 0 and 1.
The number of '0' values is 1000 and the number of '1' values is 3000.
For XGBClassifier, LGBMClassifier and CatBoostClassifier I found that there is a parameter called "scale_pos_weight" which enables to modify the weights of the class values:
scale_pos_weight = number_of_negative_values / number_of_positive_values
My question is: how can we know which value of class variable is positive and which negative?
For binary classification imbalanced dataset, always consider positive value to the minority class (class 1) and negative values to the majority class (class 0).
But you have assumed class 0 as minority class & class 1 as majority class.
By default value of scale_pos_weight=1 or > 1