This workflow compares the performances of three different setups for a classification model that is used to detect fraud in credit card data.
Scenario 1: A classification model is trained on imbalanced data
Scenario 2: A classification model is trained on resampled, balanced data.
Scenario 3: A classification model is trained on resampled, balanced data, and the predicted class probabilities are adjusted according to the class distribution in the original data
Performance is evaluated in terms of cost reduction compared to not using any model.
URL: Learn to Deal with Imbalanced Dataset Classification https://www.knime.com/blog/correcting-predicted-class-probabilities-in-imbalanced-datasets
URL: Scoring Metrics eBook https://www.knime.com/knimepress/scoring-metrics-evaluating-machine-learning-models
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
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