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  <front>
    <journal-meta>
      <issn pub-type="ppub">1613-0073</issn>
    </journal-meta>
    <article-meta>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>CEUR Workshop Proceedings</string-name>
        </contrib>
        <contrib contrib-type="editor">
          <string-name>Lviv, Ukraine</string-name>
        </contrib>
      </contrib-group>
      <fpage>0000</fpage>
      <lpage>0002</lpage>
      <abstract>
        <p />
      </abstract>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1. Introduction</title>
    </sec>
    <sec id="sec-2">
      <title>2. Related Works</title>
      <p>1
3. Methods analysis</p>
      <sec id="sec-2-1">
        <title>3.1. Linear regression</title>
        <p>3.2. LASSO</p>
      </sec>
      <sec id="sec-2-2">
        <title>3.3. Gradient Boosting</title>
      </sec>
      <sec id="sec-2-3">
        <title>3.4. Decision tree</title>
      </sec>
      <sec id="sec-2-4">
        <title>3.5. Random forest</title>
      </sec>
    </sec>
    <sec id="sec-3">
      <title>4. Dataset</title>
    </sec>
    <sec id="sec-4">
      <title>Analysis of the results</title>
      <p>[12] S. Dreiseitl, L. Ohno-Machado, Logistic regression and artificial neural network classification
models: a methodology review, Journal of Biomedical Informatics 35 (2002): 352-359.
[13] Y. Amit, D. Geman, Shape quantization and recognition with randomized trees, Neural</p>
      <p>Comput (2019): 1545–1588.
[14] B. Lanz, Machine Learning in R: Expert Techniques for Predictive Analysis, Peter, 2020.
[15] M. Kuhn, K. Johnson, Applied predictive modeling, Springer, NY, 2013.
[16] L. Breiman, Random forests, Mach Learn (2021): 5-32.</p>
    </sec>
  </body>
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