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Found 23 papers, 2 papers with code
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI
As organizations increasingly rely on AI systems for decision support in sustainability contexts, it becomes critical to understand the inherent biases and perspectives embedded in Large Language Models (LLMs).
A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany
In this study, we examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information.
Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study
Tree-based learning methods such as Random Forest and XGBoost are still the gold-standard prediction methods for tabular data.
A Central Limit Theorem for the permutation importance measure
Random Forests have become a widely used tool in machine learning since their introduction in 2001, known for their strong performance in classification and regression tasks.
Is GPT-4 Less Politically Biased than GPT-3.5? A Renewed Investigation of ChatGPT's Political Biases
On the Big Five Personality Test, GPT-3. 5 showed highly pronounced Openness and Agreeableness traits (O: 85. 9%, A: 84. 6%).
AR-Sieve Bootstrap for the Random Forest and a simulation-based comparison with rangerts time series prediction
We propose the combination of RF with a residual bootstrapping technique where we replace the IID bootstrap with the AR-Sieve Bootstrap (ARSB), which assumes the DGP to be an autoregressive process.
TREE: Tree Regularization for Efficient Execution
Random forests and decision trees are shown to be a suitable model for such a scenario, since they are not only heavily tunable towards the total model size, but also offer a high potential for optimizing their executions according to the underlying memory architecture.
Behind the Screen: Investigating ChatGPT's Dark Personality Traits and Conspiracy Beliefs
This paper tries to shed light on this, providing an in-depth analysis of the dark personality traits and conspiracy beliefs of GPT-3. 5 and GPT-4.
Adapting tree-based multiple imputation methods for multi-level data? A simulation study
However, the adapted boosting approach (mixgb with cluster dummies) consistently outperforms other methods for Level-1 variables at higher missingness rates (30%, 50%).
Evaluating tree-based imputation methods as an alternative to MICE PMM for drawing inference in empirical studies
However, the performance and validity are not completely understood, particularly compared to the standard MICE PMM.
The Self-Perception and Political Biases of ChatGPT
In addition, ChatGPT's Big Five personality traits were tested using the OCEAN test and its personality type was queried using the Myers-Briggs Type Indicator (MBTI) test.
RODD: Robust Outlier Detection in Data Cubes
We propose a general type of test data and examine all methods in a simulation study.
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study
Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors.
Dataset Bias in Human Activity Recognition
It is unknown what physical characteristics and/or soft-biometrics, such as age, height, and weight, need to be taken into account to train a classifier to achieve robustness towards heterogeneous populations in the training and testing data.
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Many production processes are characterized by numerous and complex cause-and-effect relationships.
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods.
Estimating Gaussian Copulas with Missing Data
In this work we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data.
Using Sequential Statistical Tests for Efficient Hyperparameter Tuning
Hyperparameter tuning is one of the the most time-consuming parts in machine learning.
On the Relation between Prediction and Imputation Accuracy under Missing Covariates
In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine Learning based methods for both, imputation and prediction are used.
Is there a role for statistics in artificial intelligence?
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion.
Travel Time Prediction using Tree-Based Ensembles
The computational results show that the addition of this routing data can be beneficial to the model performance.
Asymptotic Unbiasedness of the Permutation Importance Measure in Random Forest Models
Due to its intuitive idea and flexible usage, it is important to explore circumstances, for which the permutation importance based on Random Forest correctly indicates informative covariates.
Who wins the Miss Contest for Imputation Methods? Our Vote for Miss BooPF
In this paper we study whether this approach can even be enhanced by other methods such as the stochastic gradient tree boosting method, the C5. 0 algorithm or modified random forest procedures.
