International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Machine Learning Library in C++

Author(s) Kaushik Nayanala, Tanay Shinde, Twisha Goyal, Ujjawal Agarwal
Country India
Abstract This project aims to develop a comprehensive and versatile machine learning library in C++ tailored to address the diverse needs of developers and researchers in the field. The library encompasses a robust set of core machine learning algorithms, encompassing supervised, unsupervised, and reinforcement learning techniques. Additionally, it incorporates essential data preprocessing tools to streamline data manipulation and feature engineering tasks, along with model evaluation capabilities crucial for assessing algorithm performance. The library's primary focus is on providing a rich suite of machine learning algorithms, This empowers users to effectively prepare data for training machine learning models. Additionally, the library provides tools for data splitting, and model evaluation to ensure reliable and robust model performance assessment.
Keywords Supervised, Unsupervised, Model evaluation, Data Splitting, Performance assessment.
Field Computer Applications
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-09
Cite This Machine Learning Library in C++ - Kaushik Nayanala, Tanay Shinde, Twisha Goyal, Ujjawal Agarwal - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19999
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.19999
Short DOI https://doi.org/gttvdc

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