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Analyzing the predictive capacity of various machine learning algorithms

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posted on 2024-11-15, 11:10 authored by Soly Mathew BijuSoly Mathew Biju
The purpose of this study is to deploy and evaluate the performance of the new age machine learning algorithms and their applicability in business environment. Three unique set of datasheets were used to evaluate the true performance of top 4 machine learning algorithms -i.e. Generalized Linear Models (GLM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Random Forests. The findings of this study revealed that although these algorithms take different way of solving classification and regression problems, they develop quite robust models by understanding and learning the hidden patterns in the datasets. The findings of this study can be usedby other companies and individuals while analyzing and solving their respective business problems. Although a number of studies exist where new-age machine learning algorithms are tested and evaluated, there are none where the performance of these algorithms was testedon different size and type of datasets.

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Citation

Biju, S. Mathew. 2018, 'Analyzing the predictive capacity of various machine learning algorithms', International Journal of Engineering and Technology, vol. 7, no. 2.27, pp. 266-270.

Journal title

International Journal of Engineering & Technology

Volume

7

Issue

2.27

Pagination

266

Language

English

RIS ID

129819

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