Results 201 to 210 of about 360,197 (329)

Random Forest

The Journal of The Institute of Image Information and Television Engineers, 2012
openaire   +2 more sources

Random Forests model selection

, 2016
Random Forests (RF) of tree classifiers are a popular ensemble method for classification. RF have shown to be effective in many different real world classification problems and nowadays are considered as one of the best learning algorithms in this context.
ORLANDI, ILENIA, ONETO, LUCA, ANGUITA, DAVIDE   +2 more
openaire   +2 more sources

Roadmap to Precision 3D Printing of Cellulose: Rheology‐Guided Formulation, Fidelity Assessment, and Application Horizons

Advanced Materials Technologies, EarlyView.
This critical review presents a comprehensive roadmap for the precision 3D printing of cellulose. Quantitative correlations link ink formulation and rheological properties to print fidelity and final material performance. This framework guides the development of advanced functional materials, from biomedical scaffolds to electromagnetic shielding ...
Majed Amini, Hadi Hosseini, Seyyed Alireza Hashemi, Mohammad Arjmand   +3 more
wiley   +1 more source

Modeling the Restricted Mean Survival Time Using Pseudo-Value Random Forests. [PDF]

Stat Med
Schenk A, Basten V, Schmid M.
europepmc   +1 more source

Modeling binding specificities of transcription factor pairs with random forests. [PDF]

BMC Bioinformatics, 2022
Antikainen AA, Heinonen M, Lähdesmäki H.   +2 more
europepmc   +1 more source

Automated Age Estimation from OPG images and Patient Records using Deep Feature Extraction and Modified Genetic-Random Forest

Gülfem ÖZLÜ UÇAN, Omar Abboosh Hussein Gwassi, Burak Kerem APAYDIN, Bahadır UÇAN   +3 more
openalex   +1 more source

Perbandingan Metode Regresi Logistik dan Random Forest untuk Klasifikasi Data Imbalanced (Studi Kasus: Klasifikasi Rumah Tangga Miskin di Kabupaten Karangasem, Bali Tahun 2017)

, 2019
Taly Purwa
openalex   +2 more sources

Printed 2.5D‐Microstructures with Material‐Specific Functionalization for Tunable Biosensing

Advanced Materials Technologies, EarlyView.
The 2.5D‐MiSENSE platform integrates a microstructured biosensor with an in‐line milking pipeline to enable real‐time detection of mastitis biomarkers during active milk flow. The system uses a 2.5D microengineered surface and patterned electrodes to enhance milk–sensor interaction.
Matin Ataei Kachouei, Benjamin Corl, Md. Azahar Ali   +2 more
wiley   +1 more source

Random forests for the analysis of matched case-control studies. [PDF]

BMC Bioinformatics
Schauberger G, Klug SJ, Berger M.
europepmc   +1 more source

RandomForestsGLS: An R package for Random Forests for dependent data. [PDF]

J Open Source Softw, 2022
Saha A, Basu S, Datta A.
europepmc   +1 more source