The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2020.0111190
PDF

Comparison of the CatBoost Classifier with other Machine Learning Methods

Author 1: Abdullahi A. Ibrahim
Author 2: Raheem L. Ridwan
Author 3: Muhammed M. Muhammed
Author 4: Rabiat O. Abdulaziz
Author 5: Ganiyu A. Saheed

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Machine learning and data-driven techniques have become very famous and significant in several areas in recent times. In this paper, we discuss the performances of some machine learning methods with the case of the catBoost classifier algorithm on both loan approval and staff promotion. We compared the algorithm’s performance with other classifiers. After some feature engineering on both data, the CatBoost algorithm outperforms other classifiers implemented in this paper. In analysis one, features such as loan amount, loan type, applicant income, and loan purpose are major factors to predict mortgage loan approvals. In the second analysis, features such as division, foreign schooled, geopolitical zones, qualification, and working years had a high impact on staff promotion. Hence, based on the performance of the CatBoost in both analyses, we recommend this algorithm for better prediction of loan approvals and staff promotion.

Keywords: Machine learning algorithms; data science; Cat-Boost; loan approvals; staff promotion

Abdullahi A. Ibrahim, Raheem L. Ridwan, Muhammed M. Muhammed, Rabiat O. Abdulaziz and Ganiyu A. Saheed, “Comparison of the CatBoost Classifier with other Machine Learning Methods” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111190

@article{Ibrahim2020,
title = {Comparison of the CatBoost Classifier with other Machine Learning Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111190},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111190},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Abdullahi A. Ibrahim and Raheem L. Ridwan and Muhammed M. Muhammed and Rabiat O. Abdulaziz and Ganiyu A. Saheed}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org