01_​Global_​Feature_​Importance_​Example

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This application is a simple example of inspecting global feature importance for binary and multiclass classification with KNIME Software. The key of this example is the Global Feature Importance component verified and developed by the KNIME Team. In this example, the Wine quality data set is partitioned to training and test samples. Then, the black box model (Neural Network) is trained on the standardly pre-processed training data using the AutoML component. The Workflow Object capturing the pre-processing and the model is provided as an input for the Global Feature Importance component together with the test data. The component provides the global feature importance according to four techniques: three interpretable Global Surrogate Models (GLM, Decision Tree, and Random Forest) and Permutation Feature Importance.

Nodes

• Column Filter127 ×
• Rule Engine Variable90 ×
• Column Rename88 ×
• Component Input86 ×
• Component Output86 ×
• Show all 137 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Data Generation
• FeatureKNIME Deep Learning - Keras Integration
• FeatureKNIME Ensemble Learning Wrappers
• FeatureKNIME H2O Machine Learning Integration
• Show all 14 modules

Links

• KNIME Integrated Deployment - KNIME.com

  www.knime.com/integrated-deployment
• Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019.

  christophm.github.io/interpretable-ml-book/
• Seven Techniques for Data Dimensionality Reduction (2015)

  www.knime.com/white-papers
• Wine quality data set (Kaggle)

  www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009