SMOTE will just create new synthetic samples from vectors. And for that, you will first have to convert your text to some numerical vector. And then use those numerical vectors to create new numerical vectors with SMOTE. But using SMOTE for text classification doesn't usually help, because the numerical vectors that are created from text are very high dimensional, and eventually using SMOTE ...
I have a highly imbalanced dataset and would like to perform SMOTE to balance the dataset and perfrom cross validation to measure the accuracy. However, most of the existing tutorials make use of o...
What is the right way to implement SMOTE() in a classification modeling process? I am really confused about how to apply SMOTE() there. Say I have the dataset split into train and test like this as a
I am using smote to balanced the output (y) only for Model train but want to test the model with original data as it makes logic how we can test the model with smote created outputs. Please ask any...
attached is the code for xgboost on ftir data with smote and smote_weights. the results based on smote is attached as image. From the confusion matrix, i understood that even after applying smote, ...
I would like to apply SMOTE to unbalanced dataset which contains binary, categorical and continuous data. Is there a way to apply SMOTE to binary and categorical data?
I would like to create a Pipeline with SMOTE() inside, but I can't figure out where to implement it. My target value is imbalanced. Without SMOTE I have very bad results. My code: df_n = df[['user_...
Now only SMOTE().fit_resample(X_train, y_train) works. Also, all imblearn objects have a fit() method defined as well but it's completely useless because everything it does is already done by fit_resample() anyway (the documentation even urges you to use fit_resample() over fit()).
The correct approach in such cases is described in detail in own answer in the Data Science SE thread Why you shouldn't upsample before cross validation (although the answer is about CV, the rationale is identical for the train/test split case as well). In short, any resampling method (SMOTE included) should be applied only to the training data and not to the validation or test ones. Given ...
from imblearn import under_sampling, over_sampling from imblearn.over_sampling import SMOTE Again, I tried to install imblearn through pip, it works for me.
