Install imbalanced-learn in Python Easily

Imbalanced-learn is a Python library for handling imbalanced datasets. It works with scikit-learn and provides tools for resampling and metrics.

This guide will show you how to install imbalanced-learn easily. We'll cover different installation methods and provide examples.

Prerequisites for imbalanced-learn

Before installing imbalanced-learn, you need Python 3.6 or later. You should also have scikit-learn installed as it's a dependency.

If you need to install scikit-learn first, use this command:

 
pip install scikit-learn

Install imbalanced-learn Using pip

The easiest way to install imbalanced-learn is with pip. Run this command in your terminal:

pip install imbalanced-learn

This will install the latest stable version. It includes all necessary dependencies.

Install imbalanced-learn with Conda

If you use Anaconda or Miniconda, you can install it from conda-forge:

conda install -c conda-forge imbalanced-learn

This method is recommended for Anaconda users as it handles dependencies better.

Verify the Installation

After installation, verify it works by importing it in Python:

 
import imblearn
print(imblearn.__version__)

0.9.0  # Example output

Basic Usage Example

Here's a simple example using SMOTE for oversampling:

 
from imblearn.over_sampling import SMOTE
from sklearn.datasets import make_classification

X, y = make_classification(n_classes=2, weights=[0.1, 0.9])
smote = SMOTE()
X_res, y_res = smote.fit_resample(X, y)

This code creates a synthetic dataset and applies SMOTE resampling.

Troubleshooting Common Issues

If you get dependency errors, try upgrading pip first:

pip install --upgrade pip

For scikit-learn conflicts, check our guide on installing XGBoost which covers similar issues.

Alternative Installation Methods

For advanced users, you can install from source:

git clone https://github.com/scikit-learn-contrib/imbalanced-learn.git
cd imbalanced-learn
pip install .

This is useful if you need the latest features not in the stable release.

Integrating with Other Libraries

imbalanced-learn works well with other ML libraries. For example, you can combine it with CatBoost or LightGBM.

Here's an example pipeline:

 
from imblearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier

pipeline = Pipeline([
    ('smote', SMOTE()),
    ('classifier', RandomForestClassifier())
])

Conclusion

Installing imbalanced-learn is straightforward with pip or conda. It's a powerful tool for handling imbalanced datasets in machine learning.

For more Python installation guides, check our articles on PyCaret NLP and other machine learning tools.