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Books on the topic 'Random Forests'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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1

Genuer, Robin, and Jean-Michel Poggi. Random Forests with R. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56485-8.

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2

1948-, Eav Bov Bang, Thompson Matthew K, and Rocky Mountain Forest and Range Experiment Station (Fort Collins, Colo.), eds. Modeling initial conditions for root rot in forest stands: Random proportions. USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, 1993.

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3

Service, United States Forest. Noxious weed management project: Dakota Prairie grasslands : Billings, Slope, Golden Valley, Sioux, Grant, McHenry, McKenzie, Ransom and Richland counties in North Dakota, Corson, Perkins and Ziebach counties in South Dakota. U.S. Dept. of Agriculture, Forest Service, 2007.

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4

Grzeszczyk, Tadeusz. Using the Random Forest-Based Research Method for Prioritizing Project Stakeholders. SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529669404.

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5

Shi, Feng. Learn About Random Forest in R With Data From the Adult Census Income Dataset (1996). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526495464.

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6

Shi, Feng. Learn About Random Forest in Python With Data From the Adult Census Income Dataset (1996). SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526499363.

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7

1941-, Hornung Ulrich, Kotelenez P. 1943-, Papanicolaou George, and Conference on "Random Partial Differential Equations" (1989 : Mathematic Research Institute at Oberwolfach), eds. Random partial differential equations: Proceedings of the conference held at the Mathematical Research Institute at Oberwolfach, Black Forest, November 19-25, 1989. Birkhäuser Verlag, 1991.

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8

Pavlov, Yu L. Random Forests. Brill Academic Publishers, 2000.

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9

Pavlov, Yu L. Random Forests. De Gruyter, Inc., 2019.

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10

Poggi, Jean-Michel, and Robin Genuer. Random Forests with R. Springer International Publishing AG, 2020.

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11

Smith, Chris, and Mark Koning. Decision Trees and Random Forests: A Visual Introduction For Beginners. Independently published, 2017.

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12

Hansch, Ronny. Handbook of Random Forests: Theory and Applications for Remote Sensing. World Scientific Publishing Co Pte Ltd, 2020.

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13

Critelli, Christian. Machine Learning : How Decision Trees Work and How They Can Be Combined into a Random Forest: Random Forests and Decision Trees Comparison. Independently Published, 2021.

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14

Youngberg, Casimira. Machine Learning for Beginners Book : Decision Trees and Random Forests Work: Classification Machine Learning Algorithms. Independently Published, 2021.

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15

Chakraborty, Suvadip. Almost Lost You in Random Forest. White Falcon Publishing, 2022.

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16

Random Lengths Yearbook, 2004: Forest Product Market Prices and Statistics (Random Lengths Yearbook). Random Lengths Pubns, 2005.

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17

Random Lengths Yearbook 2005: Forest Product Market Prices and Statistics (Random Lengths Yearbook). Random Lengths Pubns, 2006.

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18

Lengths, Random. Random Lengths Yearbook 2006: Forest Product Market Prices and Statistics (Random Lengths Yearbook). Random Lengths Pubns, 2007.

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19

Long, Bernard, Eric Robson, and Michael Chinery. Forest Animals (Random House Tell Me About Book). Random House Childrens Books, 1993.

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20

Miller, Sue. Lost in the Forest (Random House Large Print). Random House Large Print, 2005.

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21

Random Lengths Yearbook, 1999: Forest Product Market Prices and Statistics. Random Lengths Pubns, 2000.

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22

Bartel, David R. Random Lengths 1990 Directory: A Buyers' and Sellers' Guide to the Forest Products Industry (Random Lengths Directory). 4th ed. Random Lengths Pubns, 1990.

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23

Sohnesen, Thomas Pave, and Niels Stender. Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment. World Bank, Washington, DC, 2016. http://dx.doi.org/10.1596/1813-9450-7612.

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24

Anderson, Jon P. Random Lengths Buyers' and Sellers' Guide, 1993: A Directory of the Forest Products Industry. Random House Trade, 1993.

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25

Vidales, A. Machine Learning with Matlab. Supervised Learning: Knn Classifiers, Ensemble Learning, Random Forest, Boosting and Bagging. Independently Published, 2019.

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26

Elwood, Mark. Combining results from several studies: systematic reviews and meta-analyses. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682898.003.0009.

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This chapter explains systematic reviews, the PRISMA format, and meta-analysis. It discusses publication bias, outcome reporting bias, funnel plots, the issue of false positive results in small studies, along with search strategies, electronic databases, PubMed, and the Cochrane collaboration. It discusses the assessment of quality, risks of bias, limitations of meta-analysis, heterogeneity testing, effect modification, and meta-regression methods. In part two, it explains statistical methods for meta-analyses are presented, including the Mantel-Haenszel and Peto methods for individual patient

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27

Technische Zuverlässigkeit 2021. VDI Verlag, 2021. http://dx.doi.org/10.51202/9783181023778.

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Aus dem Vorwort: Durch die zunehmende Digitalisierung und Vernetzung, beispielsweise in einer Smart Factory im Kontext von Industrie 4.0, werden hohe Anforderungen an die Zuverlässigkeit, die Verfügbarkeit und die Sicherheit von Maschinen und Anlagen gestellt. Dies erfordert den konsequenten Einsatz und die ständige Weiterentwicklung von Methoden und Modellen der Zuverlässigkeitstechnik entlang des gesamten Lebenszyklus zur Planung, Entwicklung und Absicherung der Zuverlässigkeit. Die zunehmende Digitalisierung bietet durch die steigende Zugänglichkeit und Verfügbarkeit von relevanten Daten gl

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28

Alexander, Susan J. Applying random utility modeling to recreational fishing in Oregon: Effects of forest management alternatives on steelhead production in the Elk River watershed. 1995.

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29

Hornung, U., and P. Kotelenez. Random Partial Differential Equations: Proceedings of the Conference Held at the Mathematical Research Institute at Oberwolfach, Black Forest, Novem. Birkhauser, 1991.

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30

Estadísticas forestales 2004 Región de Magallanes y de la Antártica Chilena (XII Región). INFOR, 2005. http://dx.doi.org/10.52904/20.500.12220/7837.

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Contenido: El sector forestal en la Región de Magallanes y de la Antártica Chilena; Principales indicadores 2003-2004Cap.1. Indicadores macroeconómicosCap.2. Recurso forestal Uso del suelo, Superficie de Bosque Nativo por provincia y tipo forestal, Plantaciones, sector estatal y privado, pequeños propietarios, planes de manejo aprobados según comuna, viveros, producción de plantas, incendios forestales, Áreas Silvestre ProtegidasCap.3. Consumo y Producción Trozas, Madera aserrada, Empresas ForestalesCap.4. Comercio Exterior: Exportaciones forestales, productos de lenga, Empresas exportadorasCa

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31

López, César Pérez. DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES : ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES.: Examples with MATLAB. Lulu Press, Inc., 2021.

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32

Dallmeier, Francisco, Adriana Bravo, and Michael Tweddle. Amarakaeri: Connecting Biodiversity | Conectando la Biodiversidad. Open Monographs, 2021. http://dx.doi.org/10.5479/si.16847062.

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<p><b><i>This digital monograph is made available by the publisher. Print copies are available for purchase through our distributor, Penguin Random House and on Amazon.com</i></b></p> <p><em><b> </b></em></p><p>Highlighting the enormous biodiversity of the Amarakaeri Communal Reserve (ACR) and the critical role this protected area plays in the conservation of Madre de Dios, in southeastern Peru, with more than 1,700 vivid photographs, <i>Amarakaeri: Connecting Biodiversity</i> offers readers a glimpse int

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33

Random Partial Differential Equations: Proceedings of the Conference Held at the Mathematical Research Institute at Oberwolfach, Black Forest, November 19-25 1989. Birkhauser, 2014.

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34

HORNUNG, KOTELENZ, and PAPANICOLAOU. Random Partial Differential Equations: Proceedings of the Conference Held at the Mathematical Research Institute at Oberwolfach, Black Forest, November 19-25 1989. Birkhauser Verlag, 2013.

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35

Estadísticas forestales 2004 Región de La Araucanía (IX Región). INFOR, 2005. http://dx.doi.org/10.52904/20.500.12220/7838.

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Contenido: Sector forestal en la Región de la Araucanía; Equivalencias utilizadas; Relación insumo-producto; Unidades de medida; Principales indicadores 2003-2004; Flujo de productos forestales de la industria primaria IX Región; Flujo de productos forestales de Pino radiata en la industria primaria IX RegiónCap.1. Indicadores macroeconómicosCap.2. Recurso forestal Uso del suelo, Superficie de Bosque Nativo por provincia y tipo forestal, carbono almacenado, Plantaciones, disponibilidad futura de madera pino radiata y eucalipto, viveros, producción de plantas, incendios forestales, bonificacion

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36

Lovejoy, Shaun. Weather, Macroweather, and the Climate. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190864217.001.0001.

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Weather, Macroweather, and the Climate is an insider's attempt to explain as simply as possible how to understand the atmospheric variability that occurs over an astonishing range of scales: from millimeters to the size of the planet, from milliseconds to billions of years. The variability is so large that standard ways of dealing with it are utterly inadequate: in 2015, it was found that classical approaches had underestimated the variability by the astronomical factor of a quadrillion (a million billion). Author Shaun Lovejoy asks - and answers - many fundamental questions such as: Is the at

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37

Kahler González, Carlos, Carmen Gloria Quezada C., Masatoshi Watanabe, and Claudio Eugenio Zunino Avilés. Report on breeding control of quila. INFOR, 1996. http://dx.doi.org/10.52904/20.500.12220/4000.

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Para realizar una plantación comercial o una reforestación después de la cosecha, en Chile se dice que el bambú es una maldición que obstruye el funcionamiento de la tecnología forestal. El bambú está situado generalmente como un producto "no-maderero" en los países en los cuales es nativo. Como una cuestión de hecho, el gobierno chileno indica en el documento sobre cooperación técnica entre Chile y Japón lo siguiente: Un tipo de bambú llamado Quila, se extiende en el centro-sur de Chile, a la Quila le gusta el sol y se reproduce especialemente en las áreas despejadas de forma rápida, intensa

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38

Pisarenko, T. V., T. K. Kvasha, T. V. Havrys, et al. Analysis of world technological trends in the military sphere. State Institution “Ukrainian Institute of Scientific and Technical Expertise and Information”, 2021. http://dx.doi.org/10.35668/978-966-479-127-1.

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Today, the scientific and technological sphere has become the main arena of competition between countries in the world, and the use of new technologies, especially in the field of armaments, is considered as one of the most important levers of geopolitics. Such technologies are a key for expanding the capabilities of the state's defense capabilities and achieving national security goals, priory to military and military-economic, as well as scientific and technological security. Today, the identification of scientific and technological key areas of military development is used to determine the

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