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Supervised Learning, what, why, how - Coggle Diagram
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what
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Supervised learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross validation process.
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why
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Examples for use:
Image- and object-recognition.
Predictive analytics.
Customer sentiment analysis
Spam detection.
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how
Regression
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Regression is the part of supervised learning that is responsible for calculating the possibilities out of the available data. It is a method of forming the target value based on specific predictors that point out cause and effect relations between the variables.
The process of Regression can be described as finding a model for distinguishing the data into continuous real values.
In addition to that, Regression can identify the distribution movement derived from the part data.
Classification
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Classification is the process of differentiating and categorizing the types of information presented in the dataset into the discrete values. In other words, it is the “sorting out” part of the operation.
Steps
2- It recognizes certain types of entities, looks for similar elements and couples them into relevant categories.
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