AttributeError: ‘SMOTE’ object has no attribute ‘fit_sample’ ( Solved )

It’s no secret that Python is a powerful programming language. But even the best tools can be rendered useless if you don’t know how to use them properly. In this article, we will explore an error encountered by Python programmers: the AttributeError: ‘SMOTE’ object has no attribute ‘fit_sample‘.


What is smote in Python?

SMOTE stands for synthetic minority oversampling technique. If you have any oversampling problem then this module will solve your oversampling problems. It allows you to balance class distribution by increasing minority class examples by replicating them randomly.

Why ‘SMOTE’ object has no attribute ‘fit_sample’ occurs?

This error message is displayed when you try to use the SMOTE method for oversampling without first fitting the model. The ‘SMOTE’ object referred to in the error message is the Python SMOTE class, which stands for Synthetic Minority Oversampling Technique. SMOTE is a popular method for dealing with imbalanced datasets

This error is typically raised when using the imblearn.over_sampling.SMOTE class from the imbalanced-learn library. The SMOTE class is a tool used to address the class imbalance by oversampling minority classes. Essentially, it creates synthetic data points that are similar to existing minority data points.

The error occurs when the fit_sample() method is called on a SMOTE object that has not been fitted to the data. In other words, you’re trying to use the SMOTE class without first “teaching” it what data looks like.

The Solution of ‘SMOTE’ object has no attribute ‘fit_sample’ Error

The solution to this error is to simply fit the SMOTE object to data before using the fit_sample method. This can be done with the following code:


from imblearn.over_sampling import SMOTE
oversample = SMOTE()
X, y = oversample.fit_resample(X, y)

Here, we’re first importing the SMOTE class from the imblearn.over_sampling library. Next, we instantiate a SMOTE object and fit it to our data, X and y. Finally, we call the fit_sample method on our SMOTE object, which will return oversampled data that we can use for training our model.

Final Thoughts

Though the SMOTE class is a powerful tool, it can be rendered useless if not used properly. In this article, we explored how to fix the AttributeError: ‘SMOTE’ object has no attribute ‘fit_sample’ error. By fitting the SMOTE object to data before using the fit_sample method, we can avoid this error and use the SMOTE class as intended.

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