5 hasil untuk "Burk, Lukas"
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Artikel Jurnal Teks lengkap
High‐Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression
Variable selection is an important step in the analysis of high‐dimensional data, yet there are limited options for survival outcomes in the presence of competing risks. Commonly employed penalized Cox regression considers each event type separately through cause‐specific models, neglecting…
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Preprint Teks lengkap
mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon the torch package, and simplifies the definition, training, and evaluation of neural networks for…
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Preprint Teks lengkap
xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
We introduce xplainfi, an R package built on top of the mlr3 ecosystem for global, loss-based feature importance methods for machine learning models. Various feature importance methods exist in R, but significant gaps remain, particularly regarding conditional importance methods and associated…
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Preprint Teks lengkap
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to scientifically compare new and existing model classes through proper empirical evaluation…
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Preprint Teks lengkap
Machine Learning in Epidemiology
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for…