Model Interpretability
This directory contains 10 workflows.
Compute LIME
This Component is able to create a Local Interpretable Model-agnostic Explanation (LIME) to explain the predictions of any machine learning model in […]
Counterfactual Explanations (Python)
Counterfactual Explanations describe the smallest changes to the feature values required to change the prediction outcome. Those values should be intuitive […]
Fairness Scorer
This Component computes fairness metrics over an input classification model adopting the following metrics: demographic parity, equal opportunity and […]
Global Feature Importance
This component is able to compute Global Feature Importance for classification models with up to 4 different techniques. The component additionally offers […]
K-means LIME
This is an implementation of the model explanation technique developed by H2O.ai called K-LIME using the KNIME H2O Machine Learning Integration. To find […]
Local Explanation View
This component generates an interactive visualization to help the user understand their model’s behavior on a single example data point. It works in two […]
Model Simulation View
This component generates a view to interactively execute a model on an artificial data point. The view updates a visualization of the model output based on […]
Partial Dependence Pre-processing
This Component is required to sample the data to be visualized in the Partial Dependence/ICE Plot (JavaScript) node. You can select only numerical features […]
SHAP Summarizer
This Component can be used before the bottom input port of SHAP Loop Start. This technique will use k-means to summarize the validation set and create a […]
XAI View
In order to decipher the decision making process of a black-box model you can use the eXplainable Artificial Intelligence (XAI) view. The view works for […]