Results 81 to 90 of about 360,197 (329)
On the overestimation of random forest's out-of-bag error. [PDF]
PLoS ONE, 2018 The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate ...
Silke Janitza, Roman Hornung
doaj +1 more source
Random Forests: some methodological insights [PDF]
, 2008 This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001.
Genuer, Robin +2 more
core +3 more sources
Age‐Related Characteristics of SYT1‐Associated Neurodevelopmental Disorder
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives We describe the clinical manifestations and developmental abilities of individuals with SYT1‐associated neurodevelopmental disorder (Baker‐Gordon syndrome) from infancy to adulthood. We further describe the neuroradiological and electrophysiological characteristics of the condition at different ages, and explore the associations ...
Sam G. Norwitz +3 more
wiley +1 more source
Banzhaf random forests: Cooperative game theory based random forests with consistency [PDF]
Neural Networks, 2018 Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on cooperative game theory.
Jianyuan Sun +3 more
openaire +3 more sources
Discovery and Targeted Proteomic Studies Reveal Striatal Markers Validated for Huntington's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Clinical trials for Huntington's disease (HD) enrolling persons before clinical motor diagnosis (CMD) lack validated biomarkers. This study aimed to conduct an unbiased discovery analysis and a targeted examination of proteomic biomarkers scrutinized by clinical validation. Methods Cerebrospinal fluid was obtained from PREDICT‐HD and
Daniel Chelsky +8 more
wiley +1 more source
Using the rotation and random forest models of ensemble learning to predict landslide susceptibility [PDF]
, 2020 Lingran Zhao +4 more
openalex +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu +11 more
wiley +1 more source
Shared Genetic Effects and Antagonistic Pleiotropy Between Multiple Sclerosis and Common Cancers
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Epidemiologic studies have reported inconsistent altered cancer risk in individuals with multiple sclerosis (MS). Factors such as immune dysregulation, comorbidities, and disease‐modifying therapies may contribute to this variability.
Asli Buyukkurt +5 more
wiley +1 more source
Formal Hypothesis Tests for Additive Structure in Random Forests
, 2016 While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference.
Hooker, Giles, Mentch, Lucas
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu +10 more
wiley +1 more source