Abstract
Machine learning is used to analyze data from divergent perspectives, summarize it into expedient information, and use that information to predict the likelihood of future events. Classification is one of the main problems in the domain of machine learning. It is used to classify the predetermined data for a specific class and to predict the class label for unseen data. The aim here is to study various classification algorithms in machine learning applied to the considered Census income dataset. The algorithms used for this analysis are Logistic Regression, KNN Classifier, Decision Tree, Random Forest, AdaBoost Classifier, Support Vector Machine, Gradient Boosting Classifier, and Xtrim Gradient Boosting classifier. The performance is analyzed using various metrics such as Precision, Recall, F1-score, Accuracy, Macro Average, Weighted average, and ROC Area. It has been observed that the performance of the AdaBoost classifier, Gradient Boosting classifier, and Xtrim Gradient Boosting classifier is better than other algorithms for the considered dataset. The experimental analysis shows that the AdaBoost classifier, Gradient Boosting classifier, and Xtrim Gradient Boosting classifier are giving the best scores in terms of diversified contemplated performance measures.
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Jahnavi, Y., Balasaraswathi, V.R., Nagendra Kumar, P. (2023). Model Building and Heuristic Evaluation of Various Machine Learning Classifiers. In: Pandit, M., Gaur, M.K., Kumar, S. (eds) Artificial Intelligence and Sustainable Computing. ICSISCET 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1431-9_30
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DOI: https://doi.org/10.1007/978-981-99-1431-9_30
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