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