DEAGO : negative values for categorical features inside the data about smote_variants HOT 3 OPEN

Hi, only a handful of oversampling techniques considers categorical variables, and even so, it is not implemented in the smote-variants package. Most of the oversampling techniques operate in the Euclidean space, treating all attributes continuous. A commonly followed way to use oversampling techniques with categorical variables is encoding the categorical variables, for exampleyusing one-hot encoding. Then, oversamoling techniques might end up in feature values which are fractional numbers, but from the regression point of view it is not a problem as it just expresses that the samole might be somewhere between the two categories.

Alternatively, omce the one-hot encoding is done and the oversampling is applied, you might convert the oversampled fractional values to crisp binary ones to keep the categorical nature.

from smote_variants.

SMOTENC is just a hack to apply SMOTE to categorical data. If you encode your categorical features by one-hot encoding and standardize the continuous features to have the standard deviation 1, vanilla SMOTE and all other smote variants (including DEAGO) will operate in the same metric space as SMOTENC. So there is no need for special arguments to pass categorical features, you just need to encode them properly.

from smote_variants.

Since I found SMOTENC from imbalanced learn library which can take cat_feature index as input, I thought this libraray too have some attributes to mention about the cat_features.

from smote_variants.