Results 61 to 70 of about 67,103 (261)

Generalized random forests [PDF]

The Annals of Statistics, 2019
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method considers a weighted set of nearby ...
Athey, Susan, Tibshirani, Julie, Wager, Stefan   +2 more
openaire   +3 more sources

Evidential Random Forests

Expert Systems with Applications, 2023
In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision.
Hoarau, Arthur, Martin, Arnaud, Dubois, Jean-Christophe, Le Gall, Yolande   +3 more
openaire   +1 more source

Tumor B‐cell infiltration in platinum‐treated advanced muscle‐invasive urothelial carcinoma

Molecular Oncology, EarlyView.
Bladder tumors with higher pretreatment memory B‐cell infiltration were linked to longer survival after cisplatin chemotherapy, but not carboplatin. These tumors also showed more organized immune structures (tertiary lymphoid structures) and a shared pro‐inflammatory B‐cell‐rich community, suggesting that memory B cells may help identify patients most ...
Konrad Stawiski, Júlia Perera‐Bel, Alejo Rodriguez‐Vida, Nuria Juanpere, Jihyun Lee, Daniel E. Michaud, Jennifer L. Guerriero, Kent W. Mouw, Aristotelis Bamias, Filipe L. F. Carvalho, Joaquim Bellmunt   +10 more
wiley   +1 more source

Advancing Psychological Research With Random Forests: A Review of Methods, Tools, and Applications

Advances in Methods and Practices in Psychological Science
Contemporary psychological research increasingly involves machine-learning techniques, including random forests, for their capability in analyzing complex, high-dimensional data sets and modeling nonlinear predictive relations.
Yi Feng, Han Du, Jiarui Song, Yina Sun, Yiting Wang, Aedan Joel   +5 more
doaj   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Joints in Random Forests

CoRR, 2020
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nature, however, they lack principled methods to process inputs with missing features or to detect outliers, which requires pairing them with ...
Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos   +2 more
openaire   +4 more sources

Accurate and noninvasive prostate cancer detection using plasma‐derived extracellular vesicle RNA

FEBS Open Bio, EarlyView.
Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Hanping Wei, Haoran Wu, Wei Feng
wiley   +1 more source

Temperature impact on the economic growth effect: method development and model performance evaluation with subnational data in China

EPJ Data Science, 2023
Temperature-economic growth relationships are computed to quantify the impact of climate change on the economy. However, model performance and differences of predictions among research complicate the use of climate econometric estimation.
Yu Song, Zhihua Pan, Fei Lun, Buju Long, Siyu Liu, Guolin Han, Jialin Wang, Na Huang, Ziyuan Zhang, Shangqian Ma, Guofeng Sun, Cong Liu   +11 more
doaj   +1 more source

Coalescent Random Forests

Journal of Combinatorial Theory, Series A, 1999
Suppose that rooted forests (in which the edges in each tree are directed away from the root of the tree) are formed by starting with a set of \(n\) labelled vertices and succesively adding an edge \(uv\) from a randomly chosen vertex \(u\) to the root \(v\) of a randomly chosen tree not containing \(u\). The author derives several enumeration formulae
openaire   +1 more source

Enriched random forests [PDF]

Bioinformatics, 2008
Abstract Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly.
Dhammika Amaratunga, Javier Cabrera, Yung-Seop Lee   +2 more
openaire   +2 more sources