Quantifying Model Complexity via Functional Decomposition for Better
Post-Hoc Interpretability

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

8 April 2019

Christoph Molnar

Giuseppe Casalicchio

Papers citing "Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability"

10 / 10 papers shown

Title
Statistical inference using machine learning and classical techniques based on accumulated local effects (ALE) Chitu Okoli 18 3 0 15 Oct 2023
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models Lennart Schneider B. Bischl Janek Thomas 30 6 0 17 Jul 2023
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives Matthias Feurer Katharina Eggensperger Eddie Bergman Florian Pfisterer B. Bischl Frank Hutter 51 5 0 08 Dec 2022
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction Montgomery Flora Corey K. Potvin A. McGovern Shawn Handler FAtt 28 4 0 18 Nov 2022
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models Alexander Stevens Johannes De Smedt XAI FaML 19 12 0 30 Mar 2022
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 85 455 0 13 Jul 2021
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 28 396 0 19 Oct 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 31 135 0 08 Jul 2020
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods Sheikh Rabiul Islam W. Eberle S. Ghafoor XAI 22 42 0 22 Nov 2019
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees Summer Devlin Chandan Singh W. James Murdoch Bin Yu FAtt 19 14 0 18 May 2019