Titlebook: Machine Learning and Knowledge Extraction; 6th IFIP TC 5, TC 12 Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2022

Palter

,Global Interpretable Calibration Index, a New Metric to Estimate Machine Learning Models’ Calibratine. However, existing calibration metrics can be difficult to interpret and are affected by theoretical limitations. In this paper, we present a new metric, called GICI (Global Interpretable Calibration Index), which is characterized by being local and defined only in terms of simple geometrical pri

虚度

,The ROC Diagonal is Not Layperson’s Chance: A New Baseline Shows the Useful Area,ve in a Receiver Operating Characteristic (ROC) plot and a quantity known as the area under the ROC curve (AUC or AUROC). In ROC plots, the main diagonal is often referred to as “chance” or the “random line”. In general, however, this does not correspond to the layperson’s concept of chance or rando

为宠爱

,Debiasing MDI Feature Importance and SHAP Values in Tree Ensembles, variable-importance measure in random forests, Gini importance. In particular, we demonstrate a common thread among the out-of-bag based bias correction methods and their connection to local explanation for trees. In addition, we point out a bias caused by the inclusion of inbag data in the newly d

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傻

感激小女

,Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Rine rendering process is complicated and takes a significant amount of time, not only in the rendering itself but in the setting of the scene as well. Materials, lights and cameras need to be set in order to get the best quality results. Nevertheless, the optimal output may not be obtained in the firs

ABIDE

,Semantic Causal Abstraction for Event Prediction,is often useful to reduce the complexity of the causal connections by producing an abstracted version of the graph. In this paper, we introduce semantic causal abstractions, a scheme for constructing abstracted causal graphs in order to provide a domain-independent approximation to formal causal abs

水汽

,An Evaluation Study of Intrinsic Motivation Techniques Applied to Reinforcement Learning over Hard notable. Among the numerous approaches proposed to deal with these hard exploration problems, intrinsic motivation mechanisms are arguably among the most studied alternatives to date. Advances reported in this area over time have tackled the exploration issue by proposing new algorithmic ideas to ge

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cataract

Evaluating the Performance of SOBEK Text Mining Keyword Extraction Algorithm,ised keyword extraction algorithm. Both algorithms identify keywords from single documents using mainly a statistical method, providing context independent information. The article describes briefly previous uses of SOBEK in the literature, and presents a detailed description of its text mining algo