A Comparative Study on Various Binary Classification Algorithms and their Improved Variant for Optimal Performance

Machine Learning (ML) has become a vast umbrella of various algorithms. Certainly, even for classification models, there are numerous algorithms such as Logistic Regression, Naïve Bayes Classifier, K-Nearest Neighbors, Decision tree and Random Forest Classifiers. The proposed works present a comparative study of various binary classifier and have implemented various boosting algorithms and finally have summarized the related arguments for optimal performance of the presented classification models.