03_​Adjusting_​Class_​Probabilities_​after_​Resampling

0 ×

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

Nodes

• Math Formula9 ×
• Merge Variables4 ×
• Rule Engine4 ×
• Table Row to Variable4 ×
• Table Row to Variable4 ×
• Show all 20 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Column Expressions (legacy)
• FeatureKNIME JavaScript Views (Labs)
• FeatureKNIME Javasnippet
• FeatureKNIME Math Expression (JEP)

Links

• No links available