Supervise, classification and regression

The name “supervised” learning originates from the idea that training a machine while using this type of approach is similar to how humans are learning under the supervision of a teacher.

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In supervised learning, we train the machines by providing them a set of examples. Each provided example is a pair consisting of an input object and the desired output value for that object. It is called a labeled dataset. The fact that both the input and output values are known qualifies the dataset as “labeled”.

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A trained model is used for mapping labeled data as input into a predicted output.

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The aim of a supervised learning algorithm is to find the best mapping function (f) that will be used to map the input variable(x) with the output variable(y) based on the training data.

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The two typical tasks in supervised learning are clustering and regression.

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The task of identifying if the color of a flower is “Yellow”, “Green”, ”Red”, “Blue” is an example of multiclass classification.

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To be able to predict a continuous number like the price of a product, one of the relevant types of an algorithm is based on regression.

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Regression is a set of statistical methods for estimating the strength of the relationship between a dependent variable and one or more independent variables. Such relationships can be linear or nonlinear.

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