Machine learning algorithm has brought the augmenting change in the field of artificial intelligence, which espoused human discerning power in a splendid manner. The algorithm has various categories among which classification is the most popular part. Support vector machine algorithm, logistic regression, naïve bays algorithm, decision tree, boosted tree, random forest and k nearest neighbor algorithm are all under classification of algorithms. Classification process needs some pre-defined method, which leads for choosing the train data from the sample data given by the user. Decisionmaking is the heart of any classification algorithm as supervised learning stands out on the decision of the users. Hence, a strong mathematical model based on conditional probability lies behind each algorithm. This paper is a study of those mathematical models and logic behind various classification algorithms, which help to create strong decision criteria for users to make the training dataset based on which machine can predict the proper output.


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