JKISeason3-18

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This is an example for computing explanation using LIME.
AutoML component was used to pick the best model, but any model and its set of Learner and Predictor nodes can be used.

- Read the dataset about wines
- Partition the data in train and test
- Pick few test set instances rows to explain
- Create local samples for each instance in the input table (LIME Loop Start)
- Score the samples using a workflow executor along with AutoML component
- Compute LIMEs, that is local model-agnostic explanations by training a local GLM using the samples and extracting the weights.
- Visualize them in the Composite View (Right Click > Open View)

URL: LIME - Local Interpretable Model-Agnostic Explanations, Marco Tulio Ribeiro, Blog Post https://homes.cs.washington.edu/~marcotcr/blog/lime/
URL: Verified Components https://www.knime.com/verified-components

Nodes

• Column Filter204 ×
• Component Input136 ×
• Component Output136 ×
• Rule Engine Variable130 ×
• Column Rename104 ×
• Show all 146 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

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