Top 10 Python Libraries for Addressing Imbalanced Data in ML
Written on
Chapter 1: Understanding Imbalanced Data
Imbalanced data presents a significant hurdle in machine learning, where one class is disproportionately represented compared to others. This imbalance can result in skewed models and inadequate generalization. To tackle this problem, several Python libraries have been developed that facilitate effective handling of imbalanced datasets. This article will delve into the top 10 Python libraries dedicated to managing imbalanced data in machine learning, complete with code snippets and detailed explanations.
Section 1.1: imbalanced-learn
The imbalanced-learn library, an extension of scikit-learn, provides a range of methods for rebalancing datasets, including oversampling and undersampling techniques.
from imblearn.over_sampling import RandomOverSampler
ros = RandomOverSampler()
X_resampled, y_resampled = ros.fit_resample(X, y)
Section 1.2: SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE is a technique that generates synthetic samples to create balance within the dataset.
from imblearn.over_sampling import SMOTE
smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X, y)
Subsection 1.2.1: ADASYN (Adaptive Synthetic Sampling)
ADASYN adaptively generates synthetic samples based on the density of minority class samples.
from imblearn.over_sampling import ADASYN
adasyn = ADASYN()
X_resampled, y_resampled = adasyn.fit_resample(X, y)
Section 1.3: RandomUnderSampler
The RandomUnderSampler method randomly eliminates samples from the majority class.
from imblearn.under_sampling import RandomUnderSampler
rus = RandomUnderSampler()
X_resampled, y_resampled = rus.fit_resample(X, y)
Section 1.4: Tomek Links
Tomek Links involve removing pairs of nearest neighbors from different classes.
from imblearn.under_sampling import TomekLinks
tl = TomekLinks()
X_resampled, y_resampled = tl.fit_resample(X, y)
Section 1.5: SMOTEENN (SMOTE + Edited Nearest Neighbors)
SMOTEENN combines the SMOTE technique with Tomek Links for a comprehensive approach to oversampling and undersampling.
from imblearn.combine import SMOTEENN
smoteenn = SMOTEENN()
X_resampled, y_resampled = smoteenn.fit_resample(X, y)
Section 1.6: SMOTETomek (SMOTE + Tomek Links)
Similar to SMOTEENN, SMOTETomek merges the SMOTE and Tomek Links methods for a balanced dataset.
from imblearn.combine import SMOTETomek
smotetomek = SMOTETomek()
X_resampled, y_resampled = smotetomek.fit_resample(X, y)
Section 1.7: EasyEnsemble
EasyEnsemble is an ensemble technique that constructs balanced subsets from the majority class.
from imblearn.ensemble import EasyEnsembleClassifier
ee = EasyEnsembleClassifier()
ee.fit(X, y)
Section 1.8: BalancedRandomForestClassifier
This classifier blends random forests with balanced subsamples to enhance performance.
from imblearn.ensemble import BalancedRandomForestClassifier
brf = BalancedRandomForestClassifier()
brf.fit(X, y)
Section 1.9: RUSBoostClassifier
RUSBoostClassifier combines random undersampling with boosting to improve model accuracy.
from imblearn.ensemble import RUSBoostClassifier
rusboost = RUSBoostClassifier()
rusboost.fit(X, y)
Chapter 2: The Importance of Handling Imbalanced Data
Effectively managing imbalanced data is crucial for developing precise machine learning models. These Python libraries offer various techniques to address this challenge, allowing you to select the best approach based on your specific dataset and issues.
The first video provides a comprehensive tutorial on managing imbalanced datasets in machine learning using TensorFlow and Python. It emphasizes practical applications and techniques.
The second video outlines seven effective strategies for handling imbalanced data in Python, offering insights that can enhance your machine learning projects.
? FREE E-BOOK ?: If you're interested in further exploring strategies for managing imbalanced data and other machine learning topics, don't miss our free e-book filled with valuable insights and tips.
? BREAK INTO TECH + GET HIRED: Aspiring to enter the tech field and secure your dream job? Discover more about opportunities and resources here.
If you enjoyed this article and seek more content like it, consider following us! ?In Plain English
Thank you for being part of our community! Before you leave, be sure to clap and support the writer! ? Explore even more content at PlainEnglish.io ? Sign up for our free weekly newsletter. ?? Connect with us on Twitter(X), LinkedIn, YouTube, and Discord.