Ensemble Oversampling and Under Sampling For Imbalanced Classification Using Python
Ensemble oversampling and under-sampling combines ensemble tree models with over and under-sampling techniques to improve imbalanced classification results.
This tutorial uses the Python library imblearn to compare different ensemble oversampling and under-sampling models, and choose the best model for the imbalanced dataset. You will learn
👉 How to use balanced random forest classifier?
👉 How to use random under-sampling boosting classifier?
👉 How to use easy ensemble classifier with ada boost?
👉 How to use balanced bagging classifier with Near Miss under-sampling?
👉 How to use balanced bagging classifier with SMOTE?
👉 How to pick the best model based on performance metrics?
⏰ Timecodes ⏰
0:00 - Intro
0:54 - Step 1: Install And Import Libraries
1:57 - Step 2: Create Imbalanced Dataset
2:32 - Step 3: Train Test Split
3:07 - Step 4: Baseline Random Forest Model
3:40 - Step 5: Balanced Random Forest Classifier
4:16 - Step 6: Random Under-Sampling Boosting Classifier
4:30 - Step 7: Easy Ensemble Classifier for Ada Boost Classifier
4:59 - Step 8: Balanced Bagging Classifier - Near Miss Under Sampling
5:33 - Step 9: Balanced Bagging Classifier - SMOTE
5:49 - Step 10: Use Best Model On Training Dataset
6:29 - Summary
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