!!! Overview
[{$pagename}] that is a set of [examples] used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the [Machine Learning model].

[{$pagename}] is used in [Supervised Learning] and consist of a set of training [examples] where the desired output (or [Classification]) is already known.

The [Machine Learning model] (e.g. a [Artificial Neural network] or a [Naive Bayes] [classifier]) is trained on the [{$pagename}] using a [supervised Learning] method (e.g. [gradient descent] or stochastic [gradient descent]). 

In practice, the [{$pagename}] often consist of pairs of an input [vector] and the corresponding answer [vector] or [scalar], which is commonly denoted as the target. The current model is run with the [{$pagename}] and produces a result, which is then compared with the target, for each input vector in the [{$pagename}]. Based on the result of the comparison and the specific learning [algorithm] being used, the parameters of the model are adjusted. The [Machine Learning model] fitting can include both variable selection and parameter estimation.

!! More Information
There might be more information for this subject on one of the following:
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* [#1] - [Training,Test,and Validation Sets|Wikipedia:Training,_test,_and_validation_sets|target='_blank'] - based on information obtained 2017-11-25-