Understand the business problem and the dataset to choose best evaluation metric for the problem
Create imbalanced data classification model using SMOTE data resampling technique
Compute to ROC curve and use to adjust probability threshold
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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Loading and understanding the dataset
Exploring the dataset
Evaluation metric selection
Creating a baseline model
Resampling techniques for imbalanced datasets
Implementing ROC curve
Adjusting probability threshold
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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.
Amazing course!! Thanks to the teacher for making contents easy to understand and incur the knowledge....
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