Treffer: Exploring the Basics of Decision Trees: A Comparative Analysis with Linear Regression
Title:
Exploring the Basics of Decision Trees: A Comparative Analysis with Linear Regression
Authors:
Publisher Information:
Zenodo
Publication Year:
2024
Collection:
Zenodo
Document Type:
Fachzeitschrift
text
Language:
unknown
Relation:
2581 - 7175; https://zenodo.org/records/10501151; oai:zenodo.org:10501151; https://doi.org/10.5281/zenodo.10501151
DOI:
10.5281/zenodo.10501151
Rights:
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number:
edsbas.B61FD766
Database:
BASE
Weitere Informationen
In the vast realm of machine learning, foundational algorithms like Decision Trees and Linear Regressionserve as vital entry points for newcomers. This paper undertakes a comparative study of these twoalgorithms using the Boston Housing dataset. While Decision Trees are lauded for their visual clarity andinterpretability, Linear Regression offers a slight edge in performance, as indicated by metrics such asMean Absolute Error (MAE) and the R^2 score. The choice between the two algorithms, therefore, hingeson the task's specific needs and objectives. This research aims to offer a clear perspective to beginners,emphasizing the significance and applicability of each algorithm in real-world scenarios.