Controlling Class Weights For Imbalanced Classification | Machine Learning

Controlling class weight is one of the widely used methods for imbalanced classification models in machine learning and deep learning. It modifies the class weights of the majority and minority classes during the model training process to achieve better model results.

Unlike the oversampling and under-sampling methods, the balanced weights methods do not modify the minority and majority class ratio. Instead, it penalizes the wrong predictions on the minority class by giving more weight to the loss function.

In this tutorial, we will compare logistic regression and random forest models, and how balanced weight impacts the model performance for the imbalanced classification. We will cover

? Baseline random forest model
? Baseline logistic regression model
? Balanced weight for random forest model
? Balanced subsample weight for random forest model
? Balanced weight for a logistic regression model

⏰ Timecodes ⏰
0:00 - Intro
1:03 - Step 1: Import Libraries
1:40 - Step 2: Create Imbalanced Dataset
2:16 - Step 3: Train Test Split
2:51 - Step 4: Baseline Random Forest Model
3:26 - Step 5: Algorithm Behind The Weights
4:05 - Step 6: Random Under-Sampling Boosting Classifier
4:51 - Step 7: Random Forest Classifier: Balanced Subsample Class Weight
5:29 - Step 8: Balanced Weights For Logistic Regression
6:05 - Step 9: Use The Best Model On Training Dataset
6:26 - Summary

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