Using SageMaker's linear classifier to detect fraud. Addressing class imbalance and setting target metrics for Precision and Recall
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Updated
Jul 3, 2019 - Jupyter Notebook
Using SageMaker's linear classifier to detect fraud. Addressing class imbalance and setting target metrics for Precision and Recall
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Most existing classification approaches assume the underlying training set is evenly distributed but many real-world classification problems have an imbalanced class distribution, such as rare disease identification, fraud detection, spam detection, churn prediction, electricity theft & pilferage etc.
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