LIME Loop Nodes with a Custom Regression Model

Workflow

LIME Loop Nodes with a Custom Regression Model

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An XGBoost Tree Ensemble Regression 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

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