JKISeason3-18 arief_rama
<p><strong>Explaining Cancer Predictions</strong></p><p><strong>Challenge 18</strong></p><p><br><strong>Level: </strong>Hard<br><br><strong>Description: </strong>You work as a researcher creating models to identify whether a breast tumor is benign or malign, based on anonymized patient data. Besides obtaining a classifier that works very well for both benign and malign cases, you must be able to explain how different feature values impact your results. Experiment with <strong>LIME</strong> and visualization techniques to explain your predictions and make your research more transparent. <strong>Hint:</strong> Learn more about this problem's data attributes <strong>here</strong>.</p>
Nodes
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Data Generation
- FeatureKNIME Deep Learning - Keras Integration
- FeatureKNIME Ensemble Learning Wrappers
- FeatureKNIME H2O Machine Learning Integration
- FeatureKNIME Integrated Deployment
- FeatureKNIME JavaScript Views
- FeatureKNIME Javasnippet
- FeatureKNIME JSON-Processing
- FeatureKNIME Machine Learning Interpretability Extension
- FeatureKNIME Math Expression (JEP)
- FeatureKNIME Optimization extension
- FeatureKNIME PMML Preprocessing Applier Nodes
- FeatureKNIME Quick Forms
- FeatureKNIME XGBoost Integration
- FeatureVernalis KNIME Nodes
Links
- No links available