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SMOTE (Synthetic Minority Over-sampling Technique)

SMOTE（Synthetic Minority Over-sampling Technique）は、不均衡なデータセットの問題に対処するために開発されたオーバーサンプリングの手法です。オーバーサンプリングの主な目的は少数クラスのサンプル数を増加させることにより、クラス間のバランスを改善し ...
GitHub

Applying Logistic Regression and SMOTE for Analyzing and Predicting Customer Churn in ...

This project explores the issue of customer churn in the advertising industry, particularly focusing on the challenges of dealing with imbalanced data. The analysis includes Logistic Regression as the ...
GitHub

Project ML Question1 _SMOTE_MariaJasmine_11072021.ipynb

"Model_Bag_Smote = BaggingClassifier(base_estimator=RandomForestClassifier(n_estimators=50,random_state=1,max_depth=4,min_samples_leaf=15,\n", " min_samples_split=45 ...
Hacker

Application of Synthetic Minority Over-sampling Technique (SMOTe) for Imbalanced Data-sets

Your browser does not support the audio element. Training a Machine Learning Model with this imbalanced dataset, often causes the model to develop a certain bias ...