LIME Loop Nodes with a Custom Model

This is an example for computing explanation using LIME.
An XGBoost model was picked, 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 the predictor node and a trained model
- 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)

External resources

• Data Source: UCI - Wine Quality Data Set
• LIME - Local Interpretable Model-Agnostic Explanations, Marco Tulio Ribeiro, Blog Post