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            <name>Title</name>
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                <text>Articles</text>
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    <name>Article</name>
    <description>Faculty Publications -Articles</description>
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      <name>Dublin Core</name>
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          <name>Title</name>
          <description>A name given to the resource</description>
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              <text>A comparative study on decision tree and random forest using konstanz information miner (KNIME)</text>
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        <element elementId="49">
          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>Accuracy statistics; Classification; Confusion Matrix; Data Mining; Decision Tree; DECISION TREE; KNIME; Random Forest</text>
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          <name>Description</name>
          <description>An account of the resource</description>
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              <text>With vast amounts of data floating around everywhere, it is imperative to comprehend and draw meaningful insights from the same. With the proliferation of Internet and Information Technology, data has been increasing exponentially. The 5 Vs of data i.e. Value, volume, Velocity, variety and veracity will only make sense if we are able to examine the data and uncover the hidden, yet meaningful insights. With large data becoming a norm, a lot of data mining algorithms are available that help in data mining. We have tried to compare two classification algorithms, primarily Decision trees and Random forest. A total of 10 datasets have been taken from UCI Repository and Kaggle and with the help of Konstanz Information Miner (KNIME) workflows, a comparative performance has been made pertaining to the accuracy statistics of Random Forest and decision Tree. The results show that Random Forest gives better and accurate results for a dataset as compared to decision trees.  2020 SERSC.</text>
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          <name>Creator</name>
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              <text>Khanna A.; Dey N.</text>
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          <name>Source</name>
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              <text>International Journal of Advanced Science and Technology, Vol-29, No. 5, pp. 2365-2376.</text>
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          <name>Publisher</name>
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              <text>Science and Engineering Research Support Society</text>
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          <name>Date</name>
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              <text>2020-01-01</text>
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          <name>Identifier</name>
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              <text>&lt;a href="" target="_blank" rel="noreferrer noopener"&gt;&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083863976&amp;amp;partnerID=40&amp;amp;md5=70d8435ba1924ba38f1848da283b8a86" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083863976&amp;amp;partnerID=40&amp;amp;md5=70d8435ba1924ba38f1848da283b8a86&lt;/a&gt;</text>
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          <name>Rights</name>
          <description>Information about rights held in and over the resource</description>
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              <text>Restricted Access</text>
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          <name>Relation</name>
          <description>A related resource</description>
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              <text>ISSN: 20054238</text>
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          <name>Format</name>
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              <text>Online</text>
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          <name>Language</name>
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              <text>English</text>
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          <name>Type</name>
          <description>The nature or genre of the resource</description>
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              <text>Article</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Khanna A., Department of Commerce CHRIST (Deemed to be University), Bangalore, 560029, India; Dey N., Department of Commerce CHRIST (Deemed to be University), Bangalore, 560029, India</text>
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