17_Machine_Learning_Interpretability
This directory contains 8 workflows.
01_Compute_LIMEs
Compute Local Model-agnostic Explanations (LIMEs) This is an example for computing explanation using LIME. An XGBoost model was picked, but any model […]
02_Partial_Dependence_Pre-processing
Partial Dependence Plot Example This is an example for visualizing a partial dependence plot and an ICE curves plot in KNIME. An XGBoost model was […]
03_Titantic_Prediction_Explanations
Model Interpretability, Titanic The workflow demonstrates how to use SHAP, Shapley Values and LIME implemenatations in KNIME 4.0 and generates a basic […]
04_SHAP_and_Shapley_Values
This worflow shows how to use SHAP and Shapley Values Loop nodes and it creates Stacked Bar Charts similar to Force Plots for you to compare and understand […]
05_Interactive_MLI_Composite_View
Interactive MLI Composite View This worflow will show how to use the interactive views of JavaScript nodes to visualize in a single Composite View a […]
Classification Model Evaluation using Giskard
URL: Giskard https://www.giskard.ai/products/open-source
Evaluation of ML Workflows with Giskard
<p></p><p>This workflow uses the <strong>Giskard Scanner</strong> node to scan a machine learning workflow for common weaknesses.</p><p>You can download and […]
Regression Model Evaluation using Giskard
<p>This workflow uses the <strong>Giskard Scanner node</strong> to scan a machine-learning workflow for common weaknesses.</p><p>The idea of the Giskard […]