The error message "'smote' object has no attribute 'fit_sample'" is typically encountered when using the imbalanced-learn library in Python, specifically when working with the SMOTE (Synthetic Minority Over-sampling Technique) class for handling imbalanced datasets. This error occurs because there is an attempt to call a method fit_sample that doesn't exist on the smote object.
In the context of the imbalanced-learn library, the method fit_sample was used in older versions. However, it has since been deprecated and replaced with the method fit_resample. If you're following an older tutorial or code snippet that uses fit_sample, you will need to update your code to use the new method name.
To correct this error, replace any calls to fit_sample with fit_resample. Here's an example of how you would typically use SMOTE with the updated method:
from imblearn.over_sampling import SMOTE smote = SMOTE() X_resampled, y_resampled = smote.fit_resample(X, y)
Make sure you have the latest version of the imbalanced-learn library, where fit_resample is the correct method to use. You can update imbalanced-learn using pip with the following command:
pip install -U imbalanced-learn
If you are working with a custom implementation or a subclass of SMOTE and expecting a fit_sample method, you will need to update your custom class. Ensure that it either implements fit_sample or changes to use the fit_resample method from the SMOTE base class.
By following these steps and updating your method call to fit_resample, you should resolve the error 'smote' Object Has No Attribute 'fit_sample' and be able to continue using SMOTE to balance your dataset.
