A Comparative Study of Supervised Machine Learning Algorithm
DOI:
https://doi.org/10.26438/ijcse/v6i12.875878Keywords:
Supervised Machine Learning, Classifier, Error RateAbstract
Machine Learning is a process which begins with observations of data to make better decisions of new data in future. Machine Learning algorithms divides as Supervised Machine Learning, Unsupervised Machine Learning, Semi- Supervised Machine Learning and Reinforcement Learning. In this paper, we focus on Supervised Machine Learning Algorithms especially its error rates. A Supervised learning algorithm analyses the training data and produces a classifier (conditional function), which can then be used for mapping test sets. We compare the various Supervised Machine Learning algorithms in terms of its error rates in this paper.
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