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Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). :sunflower:
Classification and Oversampling Algorithms Comparison, using Deep Feature Synthesis and Feature Selection with RFE
Testing 6 different machine learning models to determine which is best at predicting credit risk.
Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.
Data preparation, Statistical reasoning, Machine Learning
Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning
Credit Card Fraud Detection