Top 10 Python libraries for handling imbalanced data

Data imbalance is a common challenge in machine learning, where one class significantly outnumbers other classes, which can lead to biased models and poor generalization. There are various Python libraries to help handle imbalanced data efficiently. In this article, we will introduce the top ten Python libraries for handling imbalanced data in machine learning and provide code snippets and explanations for each library.

1. imbalanced-learn

imbalanced-learn is an extension library of scikit-learn, designed to provide a variety of data set rebalancing techniques. The library provides multiple options such as oversampling, undersampling, and combined methods

 from imblearn.over_sampling import RandomOverSampler  ros = RandomOverSampler() X_resampled, y_resampled = ros.fit_resample(X, y)

2, SMOTE

SMOTE generates synthetic samples to balance the data set.

from imblearn.over_sampling import SMOTE  smote = SMOTE() X_resampled, y_resampled = smote.fit_resample(X, y)

3. ADASYN

ADASYN adaptively generates synthetic samples based on the density of a few samples.

from imblearn.over_sampling import ADASYN  adasyn = ADASYN() X_resampled, y_resampled = adasyn.fit_resample(X, y)

4. RandomUnderSampler

RandomUnderSampler randomly removes samples from the majority class.

from imblearn.under_sampling import RandomUnderSampler  rus = RandomUnderSampler() X_resampled, y_resampled = rus.fit_resample(X, y)

5, Tomek Links

Tomek Links can remove pairs of nearest neighbors of different types, reducing the number of multiple samples

 from imblearn.under_sampling import TomekLinks  tl = TomekLinks() X_resampled, y_resampled = tl.fit_resample(X, y)

6, SMOTEENN (SMOTE Edited Nearest Neighbors )

SMOTEENN combines SMOTE and Edited Nearest Neighbors.

 from imblearn.combine import SMOTEENN  smoteenn = SMOTEENN() X_resampled, y_resampled = smoteenn.fit_resample(X, y)

7. SMOTETomek (SMOTE Tomek Links)

SMOTEENN combines SMOTE and Tomek Links to perform oversampling and undersampling.

 from imblearn.combine import SMOTETomek  smotetomek = SMOTETomek() X_resampled, y_resampled = smotetomek.fit_resample(X, y)

8, EasyEnsemble

EasyEnsemble is an integration method that can create balanced subsets of most classes.

 from imblearn.ensemble import EasyEnsembleClassifier  ee = EasyEnsembleClassifier() ee.fit(X, y)

9. BalancedRandomForestClassifier

BalancedRandomForestClassifier is an ensemble method that combines random forests with balanced subsamples.

 from imblearn.ensemble import BalancedRandomForestClassifier  brf = BalancedRandomForestClassifier() brf.fit(X, y)

10. RUSBoostClassifier

RUSBoostClassifier is an ensemble method that combines random undersampling and enhancement.

from imblearn.ensemble import RUSBoostClassifier  rusboost = RUSBoostClassifier() rusboost.fit(X, y)

Summary

Handling imbalanced data is crucial to building accurate machine learning models. These Python libraries provide various techniques to deal with this problem. Depending on your data set and problem, you can choose the most appropriate method to effectively balance your data.

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