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Machine Learning in Microservices
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Unlike regression models that produce a continuous output, models are considered classification models when they produce a finite output. Some examples include email spam detection, image classification, and speech recognition.
Classification models are considered versatile since they can apply to both supervised and unsupervised learning while regression models are mostly used for supervised learning. There are some regression models (such as logistic regression and support vector machine) that are also considered classification models since they use a threshold to split the output of continuous values into different categories.
Unsupervised learning is a common application used in today’s market. Although supervised learning usually performs better and provides meaningful results since we know the expected output, the majority of the data we collect is unlabeled. It costs companies time and money for human experts to sift through the...
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