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A Review Paper on Comparison of Algorithms for Supervised Machine Learning

Abhishek Panjabi, Krupali H. Shah, Vyshali Lasitha, Param Pandya

Abstract


Abstract

Supervised learning attempts to create calculations that help deliver general speculations, which is able to do forecasts about future occasions. The objective of supervised learning is to manufacture a model of the segregation of class names in terms of predictor components. The classifier produced accordingly is then used to allocate class marks to the testing data where the estimations of the indicator elements are known, yet the estimation of the class name is not known. This paper compares different supervised machine learning characterization strategies. The algorithms that are taken into considerations are K-Nearest Neighbor, Linear Regression, Logistic Regression, Naïve Bayes, Decision Trees, Random Forests and Neural Networks.

Keywords: Supervised machine learning, algorithm, K-Nearest Neighbor, Linear Regression

Cite this Article

Abhishek Panjabi, Krupali H. Shah, Vyshali Lasitha et al.  A Review Paper on Comparison of Algorithms for Supervised Machine Learning. Journal of Artificial Intelligence Research & Advances. 2017; 4(3): 11–16p.


 


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