Resampling in Supervised Fraud Detection Models

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This workflow shows the performances of four different decision tree models for fraud detection, each one trained on a different training set. One training set represents the original data with highly unbalanced target classes. The three others have been resampled to make the target classes balanced. The resampling methods that we demonstrate in this workflow are SMOTE oversampling, Bootstrap oversampling, and Bootstrap undersampling.

URL: Scoring Metrics eBook https://www.knime.com/knimepress/scoring-metrics-evaluating-machine-learning-models
URL: Resampling Imbalanced Data and Its Limits https://www.kdnuggets.com/2020/12/resampling-imbalanced-data-limits.html

Nodes

• Decision Tree Learner4 ×
• Decision Tree Predictor4 ×
• Scorer (JavaScript)4 ×
• Bootstrap Sampling2 ×
• Concatenate2 ×
• Show all 10 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME JavaScript Views (Labs)
• FeatureKNIME Javasnippet

Links

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