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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.
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)
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)
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)
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)
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)
SMOTEENN combines SMOTE and Edited Nearest Neighbors.
from imblearn.combine import SMOTEENN smoteenn = SMOTEENN() X_resampled, y_resampled = smoteenn.fit_resample(X, y)
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)
EasyEnsemble is an integration method that can create balanced subsets of most classes.
from imblearn.ensemble import EasyEnsembleClassifier ee = EasyEnsembleClassifier() ee.fit(X, y)
BalancedRandomForestClassifier is an ensemble method that combines random forests with balanced subsamples.
from imblearn.ensemble import BalancedRandomForestClassifier brf = BalancedRandomForestClassifier() brf.fit(X, y)
RUSBoostClassifier is an ensemble method that combines random undersampling and enhancement.
from imblearn.ensemble import RUSBoostClassifier rusboost = RUSBoostClassifier() rusboost.fit(X, y)
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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