Solving the 'Smote' Object Has No Attribute 'fit_Sample' Error

This document will explain the process of solving the "Smote" object has no attribute "fit_sample" error.


SMOTE (synthetic minority oversampling technique) is an algorithm used primarily in supervised machine learning to give more importance to minority classes. It is used to create artificial data points that can help to better understand the minority class (source: Analytics Vidhya).

The Error

When trying to use SMOTE on your project, you can encounter the error "Smote" object has no attribute "fit_sample," which is caused when the SMOTE module is not imported correctly.

What You Need

In order to properly use SMOTE, you will need the following:

  • A computer running Python
  • An installed version of the imbalanced-learn module

Step-by-Step Guide

Make sure you have installed the correct version of the imbalanced-learn module and that it is up to date.

Import the SMOTE module into your script:

from imblearn.over_sampling import SMOTE

Make an instance of the SMOTE() object and use the object’s fit_sample() method:

smote = SMOTE()  X_resampled, y_resampled = smote.fit_sample(X, y)

Run your code and make sure your code works without errors.


Q1: What is an "imbalanced-learn module?"

A1: The imbalanced-learn module is a Python module used for balancing datasets by using several resampling techniques. It works with various algorithms such as Random Forest, Support Vector Machine, etc.

Expert answers
for every coding challenge.

Get the information you need to solve your programming problems on, the expert-driven alternative to StackOverflow

Great! You’ve successfully signed up.

Welcome back! You've successfully signed in.

You've successfully subscribed to

Success! Check your email for magic link to sign-in.

Success! Your billing info has been updated.

Your billing was not updated.