Machine Learning with Imbalanced Data
Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.
4.70 (806 reviews)
8,943
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9.5 hours
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Sep 2024
last update
$79.99
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What you will learn
Apply random under-sampling to remove observations from majority classes
Perform under-sampling by removing observations that are hard to classify
Carry out under-sampling by retaining observations at the boundary of class separation
Apply random over-sampling to augment the minority class
Create syntethic data to increase the examples of the minority class
Implement SMOTE and its variants to synthetically generate data
Use ensemble methods with sampling techniques to improve model performance
Change the miss-classification cost optimized by the models to accomodate minority classes
Determine model performance with the most suitable metrics for imbalanced datasets
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3565567
udemy ID
10/13/2020
course created date
11/12/2020
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