A Comparative Study on Various Binary Classification Algorithms and their Improved Variant for Optimal Performance
Vedant Bahel, Sofia Pillai, Manit Malhotra
Abstract
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.
Topics & Concepts
Naive Bayes classifierComputer scienceBoosting (machine learning)Decision treeRandom forestMachine learningArtificial intelligenceLogistic model treeBinary classificationStatistical classificationClassifier (UML)Logistic regressionAlgorithmBayes classifierBayes error rateBinary numberSupport vector machineData miningMathematicsArithmeticFace and Expression RecognitionMachine Learning and Data ClassificationRemote-Sensing Image Classification