Journal of Machine Learning and Applications

Supervised and Unsupervised Machine Learning

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Supervised learning refers to the machine learning method of training models using labeled data. After being trained with a labeled dataset, the machine- learning model analyzes the features of new inputs and determines the mapping function to associate the input and output variables. Regression, Logistic Regression, Classification, Naive Bayes Classifiers, K-NN (k nearest neighbors), Decision Trees, and Support Vector Machines are various types of supervised machine learning. Supervised learning can be applied to risk assessments, image classification, fraud detection, and visual recognition in real-life settings. Unsupervised learning teaches a machine to use unlabeled, unclassified data and allows the algorithm to operate the data without monitoring. The objective of unsupervised learning is to extract structure and patterns from the input data independently. K-Means clustering, Hierarchical clustering, Apriori algorithm, Principal Component Analysis, Singular Value Decomposition, and Independent Component Analysis are some algorithms used in unsupervised learning.