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 of scikit-learn that provides various techniques for rebalancing data sets. It provides 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 dataset.
from imblearn.over_sampling import SMOTE
smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X, y)
3、Island
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、Tom Links
Tomek Links can remove nearest neighbor pairs of different classes and reduce 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 for oversampling and undersampling.
from imblearn.combine import SMOTETomek
smotetomek = SMOTETomek()
X_resampled, y_resampled = smotetomek.fit_resample(X, y)
8、EasyEnsemble
EasyEnsemble is an ensemble method that creates balanced subsets of majority classes.
from imblearn.ensemble import EasyEnsembleClassifier
ee = EasyEnsembleClassifier()
ee.fit(X, y)
9、BalancedRandomForestClassifier
BalancedRandomForestClassifier is an ensemble method that combines random forest with balanced subsampling.
from imblearn.ensemble import BalancedRandomForestClassifier
brf = BalancedRandomForestClassifier()
brf.fit(X, y)
10、RUSBoostClassifier
RUSBoostClassifier is an ensemble method that combines random undersampling and boosting.
from imblearn.ensemble import RUSBoostClassifier
rusboost = RUSBoostClassifier()
rusboost.fit(X, y)
Summarize
Handling imbalanced data is critical 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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