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A Comparative Analysis of Random For...

Harris, Chinchu.

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A Comparative Analysis of Random Forest and Logistic Regression for Weed Risk Assessment.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Comparative Analysis of Random Forest and Logistic Regression for Weed Risk Assessment./
Author:	Harris, Chinchu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	140 p.
Notes:	Source: Masters Abstracts International, Volume: 80-09.
Contained By:	Masters Abstracts International80-09.
Subject:	Ecology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10825576
ISBN:	9780438869370

A Comparative Analysis of Random Forest and Logistic Regression for Weed Risk Assessment.
Harris, Chinchu.

A Comparative Analysis of Random Forest and Logistic Regression for Weed Risk Assessment. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 140 p.

Source: Masters Abstracts International, Volume: 80-09.

Thesis (M.S.)--University of Maryland, College Park, 2018.

This item must not be sold to any third party vendors.

Invasive species have largely negative impacts on the environment and the economy. The management and regulation of invasive plants are facilitated using screening tools, such as weed risk assessments (WRAs) to predict the invasive potential of non-native plants. The identification of these species and their subsequent regulation on importation helps to reduce the risk of future ecosystem and economic costs. Globally, there are many different types of highly useful WRAs already available. However, in this day of big data and powerful predictive analytics, there is an increasing demand for the development of new and more robust screening tools. In this thesis, I use the machine learning algorithm, Random forests, to develop a new WRA. I show that random forest model has greater predictive accuracies than an existing logistic regression model and that random forest is a better learner. In addition, variable importance analysis was performed to identify factors associated with invasive status classification of non-native plants. The study suggests that random forests make powerful weed risk screening tools and should be utilized for assessing invasive risk potential along with other WRAs. An integrative approach for evaluating weed risk can greatly serve to facilitate the WRA process.

ISBN: 9780438869370Subjects--Topical Terms:

516476
Ecology.

A Comparative Analysis of Random Forest and Logistic Regression for Weed Risk Assessment.
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100 1 $a Harris, Chinchu. $3 3435476
245 1 0 $a A Comparative Analysis of Random Forest and Logistic Regression for Weed Risk Assessment.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 140 p.
500 $a Source: Masters Abstracts International, Volume: 80-09.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Peer, Wendy.
502 $a Thesis (M.S.)--University of Maryland, College Park, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a Invasive species have largely negative impacts on the environment and the economy. The management and regulation of invasive plants are facilitated using screening tools, such as weed risk assessments (WRAs) to predict the invasive potential of non-native plants. The identification of these species and their subsequent regulation on importation helps to reduce the risk of future ecosystem and economic costs. Globally, there are many different types of highly useful WRAs already available. However, in this day of big data and powerful predictive analytics, there is an increasing demand for the development of new and more robust screening tools. In this thesis, I use the machine learning algorithm, Random forests, to develop a new WRA. I show that random forest model has greater predictive accuracies than an existing logistic regression model and that random forest is a better learner. In addition, variable importance analysis was performed to identify factors associated with invasive status classification of non-native plants. The study suggests that random forests make powerful weed risk screening tools and should be utilized for assessing invasive risk potential along with other WRAs. An integrative approach for evaluating weed risk can greatly serve to facilitate the WRA process.
590 $a School code: 0117.
650 4 $a Ecology. $3 516476
650 4 $a Statistics. $3 517247
650 4 $a Plant sciences. $3 3173832
690 $a 0329
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710 2 $a University of Maryland, College Park. $b Plant Science and Landscape Architecture (PLSA). $3 3172437
773 0 $t Masters Abstracts International $g 80-09.
790 $a 0117
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10825576