{"id":7863,"date":"2022-04-25T17:52:19","date_gmt":"2022-04-25T13:22:19","guid":{"rendered":"https:\/\/nazmaran.com\/types-of-machine-learning-models\/"},"modified":"2023-09-09T11:44:50","modified_gmt":"2023-09-09T08:14:50","slug":"types-of-machine-learning-models","status":"publish","type":"post","link":"https:\/\/nazmaran.com\/en\/types-of-machine-learning-models\/","title":{"rendered":"Types of machine learning models"},"content":{"rendered":"\t\t
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The concept of machine learning<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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supervised learning<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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A variety of machine learning<\/strong><\/a> models, the learning process of the algorithm from the training data set can be considered as a teacher who supervises the learning process. We know the correct answers, the algorithm repeatedly predicts on the training data and is corrected by the teacher. Learning stops when the algorithm reaches an acceptable level of performance. In supervised learning, you provide explicit inputs to the machine learning algorithm. The algorithm knows what to learn from the data and what conclusions to expect from it. Data labeling is important for building a supervised model. Companies collect large data sets on a daily basis. Labeling these datasets to make the machine learning model easier to work with
(machine learning) Supervised learning algorithms attempt to model relationships and dependencies between output, target prediction, and input features so that we can predict new data output values \u200b\u200bbased on those relationships learned from previous data sets.
Supervised learning can be further classified into regression and classification, which are applied to time series forecasting and recommender systems.
1- Regression related to time series whose output is continuous. The ultimate goal of the regression algorithm is to draw the best line or curve between the data. The three main metrics used to evaluate the trained regression model are variance, deviation, and error.<\/p>\n

2- Classification, which outputs are grouped and discrete, and basically classifies a set of data into classes.<\/p>\n

Some popular examples of supervised machine learning algorithms are:
Linear regression for regression problems.
Random forest for classification and regression problems.
Support vector machines for classification problems.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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unsupervised learning<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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In unsupervised learning you only have access to the input data and no associated output variables. The goal of unsupervised learning is to model the structure or distribution of data in order to obtain and extract more information from the data. In unsupervised learning, unlike supervised learning, there are no right answers and no teacher. Algorithms act on their own to discover and provide structure in data.<\/p>\n

Unsupervised learning algorithms are used for data clustering and association rules based on available features.<\/p>\n