Constructing bi-plots for random forest: Tutorial

Lionel Blanchet, Raffaele Vitale, Robert van Vorstenbosch, George Stavropoulos, John Pender, Daisy Jonkers, Frederik-Jan van Schooten, Agnieszka Smolinska*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Web of Science)
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Abstract

Current technological developments have allowed for a significant increase and availability of data. Consequently, this has opened enormous opportunities for the machine learning and data science field, translating into the development of new algorithms in a wide range of applications in medical, biomedical, daily-life, and national security areas. Ensemble techniques are among the pillars of the machine learning field, and they can be defined as approaches in which multiple, complex, independent/uncorrelated, predictive models are subsequently combined by either averaging or voting to yield a higher model performance. Random forest (RF), a popular ensemble method, has been successfully applied in various domains due to its ability to build predictive models with high certainty and little necessity of model optimization. RF provides both a predictive model and an estimation of the variable importance. However, the estimation of the variable importance is based on thousands of trees, and therefore, it does not specify which variable is important for which sample group.

The present study demonstrates an approach based on the pseudo-sample principle that allows for construction of bi-plots (i.e. spin plots) associated with RF models. The pseudo-sample principle for RF. is explained and demonstrated by using two simulated datasets, and three different types of real data, which include political sciences, food chemistry and the human microbiome data. The pseudo-sample bi plots, associated with RF and its unsupervised version, allow for a versatile visualization of multivariate models, and the variable importance and the relation among them. (c) 2020 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)146-155
Number of pages10
JournalAnalytica Chimica Acta
Volume1131
DOIs
Publication statusPublished - 22 Sep 2020

Keywords

  • Random forest interpretation
  • Pseudo samples
  • Bi-plots
  • Proximity matrix
  • Principal coordinates analysis
  • PSEUDO-SAMPLE TRAJECTORIES
  • PARTIAL LEAST-SQUARES
  • FAULT-DIAGNOSIS
  • KERNEL

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