Imbalanced-learn: Essential Toolkit for Handling Imbalanced Datasets

Overview

Imbalanced-learn is a Python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of the scikit-learn-contrib projects.

Why Imbalanced-learn Matters

In real-world datasets, it’s common to have imbalanced classes where one class significantly outnumbers others. This creates challenges:

• Credit Card Fraud: 99.9% legitimate transactions vs 0.1% fraudulent
• Medical Diagnosis: Rare diseases with few positive cases
• Manufacturing Defects: Most products pass quality control
• Customer Churn: Typically only 2-5% of customers churn

Standard machine learning algorithms often fail on imbalanced datasets, predicting only the majority class.

Key Features

1. Over-sampling Methods

• SMOTE (Synthetic Minority Over-sampling Technique)
• ADASYN (Adaptive Synthetic Sampling)
• BorderlineSMOTE and variants
• Random over-sampling

2. Under-sampling Methods

• Tomek Links
• Edited Nearest Neighbors
• Condensed Nearest Neighbors
• Random under-sampling

3. Combination Methods

• SMOTEENN (SMOTE + Edited Nearest Neighbors)
• SMOTETomek (SMOTE + Tomek Links)

4. Ensemble Methods

• BalancedRandomForestClassifier
• BalancedBaggingClassifier
• RUSBoostClassifier
• EasyEnsembleClassifier

Quick Start Example

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# Assuming X and y are your features and target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Apply SMOTE to balance the training data
smote = SMOTE(random_state=42)
X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)

# Train your model on balanced data
model = LogisticRegression()
model.fit(X_train_balanced, y_train_balanced)

# Evaluate on original imbalanced test set
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

Installation

# Using pip
pip install imbalanced-learn

# Using conda
conda install -c conda-forge imbalanced-learn

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

Best Practices

1. Never Balance the Test Set

Only balance training data. Test on the original distribution to get realistic performance metrics.

2. Cross-Validation Strategy

Use StratifiedKFold to maintain class distribution across folds:

from sklearn.model_selection import StratifiedKFold
from imblearn.pipeline import Pipeline

pipeline = Pipeline([
    ('sampler', SMOTE()),
    ('classifier', LogisticRegression())
])

3. Choose the Right Technique

• Small dataset: Prefer over-sampling (SMOTE)
• Large dataset: Consider under-sampling
• Noisy data: Use cleaning methods (Tomek Links)
• Ensemble preference: Try BalancedRandomForest

4. Monitor Multiple Metrics

Don’t rely only on accuracy. Use:

• Precision, Recall, F1-score
• ROC-AUC and PR-AUC
• Confusion Matrix
• Class-specific metrics

Integration with ML Pipelines

Imbalanced-learn integrates seamlessly with scikit-learn pipelines:

from imblearn.pipeline import Pipeline as ImbPipeline
from imblearn.over_sampling import SMOTE
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

pipeline = ImbPipeline([
    ('scaler', StandardScaler()),
    ('sampler', SMOTE(sampling_strategy='auto')),
    ('classifier', RandomForestClassifier())
])

pipeline.fit(X_train, y_train)

Comparison with Alternatives

Advanced Techniques

Custom Sampling Strategy

# Define custom ratio for each class
sampling_strategy = {0: 1000, 1: 1000, 2: 500}
smote = SMOTE(sampling_strategy=sampling_strategy)

Combining with Feature Selection

from sklearn.feature_selection import SelectKBest
from imblearn.pipeline import Pipeline

pipeline = Pipeline([
    ('feature_selection', SelectKBest(k=10)),
    ('sampler', SMOTE()),
    ('classifier', LogisticRegression())
])

Common Pitfalls to Avoid

1. Data Leakage: Apply resampling only on training data
2. Over-reliance on Accuracy: Use appropriate metrics for imbalanced data
3. Ignoring Domain Knowledge: Sometimes imbalance reflects reality
4. Not Trying Cost-Sensitive Learning: Consider adjusting class weights first

Resources

• Official Documentation: https://imbalanced-learn.org/
• GitHub Repository: https://github.com/scikit-learn-contrib/imbalanced-learn
• API Reference: https://imbalanced-learn.org/stable/references/index.html
• Examples Gallery: https://imbalanced-learn.org/stable/auto_examples/index.html

Conclusion

Imbalanced-learn is an essential tool for any data scientist dealing with real-world classification problems. Its scikit-learn compatibility and comprehensive set of techniques make it the go-to solution for handling imbalanced datasets in Python.

Whether you’re detecting fraud, diagnosing diseases, or predicting customer churn, imbalanced-learn provides the tools to build more robust and fair machine learning models.