Assistant Professor, D.P. Vipra College, Bilaspur (C.G.), India
Abstract
Machine learning methods are an effective way to classify data. Machine learning is a broad and fascinating field. Even today, machine learning technology runs a substantial part of our life, often without our knowing it. Machine learning is also fascinating in its own right for the philosophical questions it raises about what it means to learn and succeed at tasks. Machine learning is about predicting the future based on the past. In machine learning, the aim is to fit a model to the data. In this paper, a study between different supervised machine learning algorithms: Support Vector Machine (SVM), Decision Tree, Naive Bayes (NB) and K Nearest Neighbors (k-NN). The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity and specificity.
Suryawanshi, I. P., Victor, V., Choubey, N., & Singh, V. (2023). A Comparative Study of Supervised Machine Learning. Int. J. of Aquatic Science, 14(1), 529-532.
MLA
Ishwar Prasad Suryawanshi; Vishwas Victor; Nidhish Choubey; Varsha Singh. "A Comparative Study of Supervised Machine Learning". Int. J. of Aquatic Science, 14, 1, 2023, 529-532.
HARVARD
Suryawanshi, I. P., Victor, V., Choubey, N., Singh, V. (2023). 'A Comparative Study of Supervised Machine Learning', Int. J. of Aquatic Science, 14(1), pp. 529-532.
VANCOUVER
Suryawanshi, I. P., Victor, V., Choubey, N., Singh, V. A Comparative Study of Supervised Machine Learning. Int. J. of Aquatic Science, 2023; 14(1): 529-532.
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