Handling Imbalanced Data Classification Problems
In this 2-hour long project-based course on handling imbalanced data classification problems, you will learn to understand the business problem related we are trying to solve and and understand the dataset. You will also learn how to select best evaluation metric for imbalanced datasets and data resampling techniques like undersampling, oversampling and SMOTE before we use them for model building process. At the end of the course you will understand and learn how to implement ROC curve and adjust probability threshold to improve selected evaluation metric of the model. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Receiver Operating Characteristic (ROC)
由 NG 提供2020年8月24日
Amazing course!! Thanks to the teacher for making contents easy to understand and incur the knowledge....
由 VT 提供2020年8月16日
Really amazing course. The basics of handling imbalance data are covered really well. Good explanation of how to work with ROC curve and get the right threshold to increase the target metrics.
由 AK 提供2020年12月4日
This is an amazing project with nice explanations! If you are into credit scoring and things of that sort, I highly recommend it. I just wished he elaborated more how to detect the threshold values