Compute Local Model-agnostic Explanations (LIMEs)

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