LIME Loop Nodes with AutoML
This is an example for computing explanation using LIME.
AutoML component was used to pick the best model, 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 a workflow executor along with AutoML component
- 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)
This is an example for computing explanation using LIME.
AutoML component was used to pick the best model, 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 a workflow executor along with AutoML component
- 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)