Results 71 to 80 of about 3,398,718 (314)
IEICE Transactions on Information and Systems, 2015 Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest
MISHINA, Yohei +4 more
openaire +3 more sources
Confusion matrix for random forest.
, 2022 Confusion matrix for random forest.
Thomas C. M. Lee (7372220) +2 more
core +1 more source
Attitudes of cancer patients toward ctDNA‐based liquid biopsy for follow‐up
Molecular Oncology, EarlyView.This mixed‐methods study explored cancer patients' experiences, knowledge, and preferences regarding ctDNA‐based liquid biopsy for follow‐up. Awareness of ctDNA was low and recall of previous liquid biopsy was imperfect, yet acceptance was high.
Siddharth Chhajlani +7 more
wiley +1 more source
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
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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 +2 more
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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 +3 more
openaire +2 more sources
The impact of leukocyte‐associated RNA retention on platelet RNA‐based cancer detection
Molecular Oncology, EarlyView.Platelets carry RNA signals that may help detect cancer, but platelet samples can also retain RNA from white blood cells. We found that this retained white blood cell signal did not significantly affect machine learning cancer classification, suggesting that the cancer‐related RNA signal in platelets is largely independent of leukocyte‐associated RNA ...
Michał Sieczczyński +12 more
wiley +1 more source
Random forest algorithm use for crop recommendation
ITEGAM-JETIA, 2023 The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender.
Pradip Mukundrao Paithane
doaj +1 more source
, 2017 Random-effects model forest plots showing the impact of allogeneic BT on the survival of PCa patients after RP.
Su-Liang Li (3704542) +2 more
core +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 +2 more
wiley +1 more source