This Component is able to create a Local Interpretable Model-agnostic Explanation (LIME) to explain the predictions of any machine learning model in KNIME.
You have to use this component together with LIME Loop Start node from the KNIME Machine Learning Interpretability Extension.
Please install KNIME H2O Machine Learning Integration.
https://hub.knime.com/knime/extensions/org.knime.features.ext.h2o/latest
The workflow within the Component will go through the following steps:
1. Using LASSO to select relevant features.
2. Training a local surrogate Generalized Linear Model (GLM) using Weighted Least Squares (WLS).
3. Output the coefficients of the local model able to explain the original instance prediction.
More info about LIME at:
homes.cs.washington.edu/~marcotcr/blog/lime
- Type: TablePredicted SamplesA table with a Double Type prediction column of the sampled instances. Such prediction column can be created scoring the LIME Loop Start top ouput table with the predictor node of your model. If your model is solving a classification task make sure to output a pobability column of Double Type.
- Type: TableTest Set Row InstancesThis table contains the data used to learn a local surrogate model including a weight column and it is produced by Loop Start node bottom port.