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Smote will just create new synthetic samples from vectors It would give you attributeerror 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 'nonetype' object has no attribute 'split' smote asked 3 years, 5 months ago modified 2 years, 9 months ago viewed 8k times 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
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 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 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 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?
The discussion with a smote implementation on gmane that i originally linked to, appears to be no longer available.
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