TreeMap 2016 Stand Size Code Algorithm (Image Service) حسب U.S. Forest Service (17476914)

"…<div>TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.…"

Improved random forest algorithm. حسب Zhen Zhao (159931)

"…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …"

Schematic diagram of random forest algorithm. حسب Imee V. Necesito (12749885)

Pseudocode for the missForestPredict algorithm. حسب Elena Albu (15181070)

"…Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. …"

Random Forest and Gradient Boosting algorithms. حسب Yvan J. Garcia-Lopez (20783591)

The schematic diagram of the iForest algorithm. حسب Zhibo Xie (6790775)

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Prediction performance compared with random forest algorithm. حسب Yixian Wen (12201388)

Determining the importance of factors in quantitative modeling by the RF algorithm. حسب Mahboobe Hojati (21702672)

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Determining the importance of factors in qualitative modeling by the RF algorithm. حسب Mahboobe Hojati (21702672)

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The basic process of feature selection of the random forest algorithm. حسب Jintao Li (448681)

The algorithm of the applied models. حسب Elif Tuçe Bal (21367965)

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Pseudocode for the missForestPredict algorithm - new observation. حسب Elena Albu (15181070)

Main code. حسب Jingguo Bi (21439766)

Predictive variables screened by LASSO regression and Random Forest algorithm. حسب Da-cheng Wang (22232009)

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General model performance for different algorithms. حسب Binghan Xue (21253235)

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Performance of models by different algorithms. حسب Yong Zhang (5893)

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Comparison of the EODA algorithm with existing algorithms in terms of recall. حسب Sunil Kumar (102321)

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Comparison of the EODA algorithm with existing algorithms in terms of precision. حسب Sunil Kumar (102321)

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Random forest model details. حسب Giuliano Lorenzoni (21440268)

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Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg حسب Junfei Zhang (7547975)

"…Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …"