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This article presents a study on classifying white and red wines using the Decision Tree Machine Learning technique. The main objective is to reliably predict the color of the wine—white or red—using physical and chemical variables as inputs to machine learning models. A 10-fold stratified cross-validation was applied to two approaches: a base model with a Decision Tree (Decision-TreeClassifier) ​​and another with an additional stage of nonlinear dimensionality reduction using KernelPCA. The best result was obtained with a five-dimensional k-fold reduction, achieving an accuracy of 99.1%.

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Classification of White or Red Wines Using the Decision Trees Machine Learning Technique

  • 会议论文

  • 发表日期：

    2026年

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  • Liana Leiva,
  • José Alberto Hernández-Aguilar,
  • Julio César Ponce-Gallegos

摘要

This article presents a study on classifying white and red wines using the Decision Tree Machine Learning technique. The main objective is to reliably predict the color of the wine—white or red—using physical and chemical variables as inputs to machine learning models. A 10-fold stratified cross-validation was applied to two approaches: a base model with a Decision Tree (Decision-TreeClassifier) ​​and another with an additional stage of nonlinear dimensionality reduction using KernelPCA. The best result was obtained with a five-dimensional k-fold reduction, achieving an accuracy of 99.1%.