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)
Used extensions & nodes
Created with KNIME Analytics Platform version 4.6.0
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