Giuseppe Casalicchio | DeepAI

DeepAI

Featured Co-authors

Frank Hutter
108 publications
Bernd Bischl
79 publications
Marius Lindauer
45 publications
Joaquin Vanschoren
36 publications
David Rügamer
31 publications
Jakob Bossek
23 publications
Pascal Kerschke
22 publications
Moritz Grosse-Wentrup
17 publications
Fabian Scheipl
12 publications
Christoph Molnar
11 publications
Michel Lang
11 publications

∙ 09/21/2022

Algorithm-Agnostic Interpretations for Clustering

A clustering outcome for high-dimensional data is typically interpreted ...

Christian A. Scholbeck, et al. ∙

∙ 06/11/2022

Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution

Despite all the benefits of automated hyperparameter optimization (HPO),...

Julia Moosbauer, et al. ∙

∙ 02/15/2022

REPID: Regional Effect Plots with implicit Interaction Detection

Machine learning models can automatically learn complex relationships, s...

Julia Herbinger, et al. ∙

∙ 01/21/2022

Marginal Effects for Non-Linear Prediction Functions

Beta coefficients for linear regression models represent the ideal form ...

Christian A. Scholbeck, et al. ∙

∙ 11/08/2021

Explaining Hyperparameter Optimization via Partial Dependence Plots

Automated hyperparameter optimization (HPO) can support practitioners to...

Julia Moosbauer, et al. ∙

∙ 09/03/2021

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process

Scientists and practitioners increasingly rely on machine learning to mo...

Christoph Molnar, et al. ∙

∙ 07/28/2021

Developing Open Source Educational Resources for Machine Learning and Data Science

Education should not be a privilege but a common good. It should be open...

Ludwig Bothmann, et al. ∙

∙ 06/15/2021

Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)

Global model-agnostic feature importance measures either quantify whethe...

Gunnar König, et al. ∙

∙ 06/04/2021

Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

In recent years, the need for neutral benchmark studies that focus on th...

Christina Nießl, et al. ∙

∙ 04/23/2021

Grouped Feature Importance and Combined Features Effect Plot

Interpretable machine learning has become a very active area of research...

Quay Au, et al. ∙

∙ 10/19/2020

Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

We present a brief history of the field of interpretable machine learnin...

Christoph Molnar, et al. ∙

∙ 07/08/2020

Pitfalls to Avoid when Interpreting Machine Learning Models

Modern requirements for machine learning (ML) models include both high p...

Christoph Molnar, et al. ∙

∙ 06/08/2020

Model-agnostic Feature Importance and Effects with Dependent Features – A Conditional Subgroup Approach

Partial dependence plots and permutation feature importance are popular ...

Christoph Molnar, et al. ∙

∙ 04/08/2019

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations

Non-linear machine learning models often trade off a great predictive pe...

Christian A. Scholbeck, et al. ∙

∙ 04/08/2019

Component-Wise Boosting of Targets for Multi-Output Prediction

Multi-output prediction deals with the prediction of several targets of ...

Quay Au, et al. ∙

∙ 04/08/2019

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition

To obtain interpretable machine learning models, either interpretable mo...

Christoph Molnar, et al. ∙

∙ 04/18/2018

Visualizing the Feature Importance for Black Box Models

In recent years, a large amount of model-agnostic methods to improve the...

Giuseppe Casalicchio, et al. ∙

∙ 08/11/2017

OpenML Benchmarking Suites and the OpenML100

We advocate the use of curated, comprehensive benchmark suites of machin...

Bernd Bischl, et al. ∙

∙ 03/27/2017

Multilabel Classification with R Package mlr

We implemented several multilabel classification algorithms in the machi...

Philipp Probst, et al. ∙

∙ 01/05/2017

OpenML: An R Package to Connect to the Machine Learning Platform OpenML

OpenML is an online machine learning platform where researchers can easi...

Giuseppe Casalicchio, et al. ∙