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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