Knowledge Tree
Random Forest
Also known as: Random Forests, RF, Random Forest model
Facts (10)
Sources
Recent breakthroughs in the valorization of lignocellulosic biomass ... pubs.rsc.org Jun 7, 2025 6 facts
claimKashyap et al. designed their Random Forest model to provide the highest possible mechanical properties for concrete with minimal resource use, aiming to reduce water pollution and land degradation.
measurementThe Random Forest model evaluated by Kashyap et al. achieved an R2-value of 0.95, a mean absolute error of 1.05 kN mm−2, and a mean square error of 5.25 kN mm−2.
claimA sensitivity analysis using a Random Forest model determined that water and cement proportions are the key factors influencing the compressive strength of reinforced concrete, while the addition of coarse aggregates negatively affects compressive strength.
claimKashyap et al. concluded that the Random Forest model is the most effective machine learning model for evaluating the compressive strength of concrete, based on testing over 54 different concrete mixes.
claimThe Random Forest model, the Bagging Regressor, and the Decision Tree model were identified as the superior machine learning models for assessing concrete compressive strength in the research cited as reference 267.
claimAnwar et al. utilized various regression machine learning models—including Random Forest, Support Vector Regressor, Gradient Boosting Regressor, Bagging Regressor, and Decision Tree—to predict the compressive strength of CNF-modified sustainable concrete composite.
The interplay of future solar energy, land cover change, and their ... discovery.researcher.life Jun 9, 2024 2 facts
procedureThe study of the lower Mahananda River basin utilized Landsat imagery and the random forest (RF) classification technique for past LULC classification, and the Cellular Automata Markov Chain (CA-MA) model for future LULC prediction.
procedureThe flood vulnerability map for the study area was generated using the Random Forest model, which incorporated historical data from 332 flooded locations and 12 geophysical and anthropogenic flood factors under land use/land cover (LULC) change scenarios.
Papers - Dr Vaishak Belle vaishakbelle.github.io 1 fact
referenceVaishak Belle and G. Lakemeyer authored the master's thesis 'Detection and Recognition of Human Faces using Random Forests for a Mobile Robot' at the Department of Computer Science, RWTH Aachen University in 2008.
Demand side management using optimization strategies for efficient ... journals.plos.org Mar 21, 2024 1 fact
referenceTools available for monitoring faults and improving system performance in energy management include extra-tree, bagging k-nearest neighbors (KNN), voting regressor, random forest, and boosting algorithms.