{"id":3417,"date":"2025-10-28T10:59:21","date_gmt":"2025-10-28T09:59:21","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/samples\/"},"modified":"2025-11-27T14:55:44","modified_gmt":"2025-11-27T13:55:44","slug":"samples","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/samples\/","title":{"rendered":"Training, validation and testing samples in machine learning"},"content":{"rendered":"\t\t
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Introduction<\/h2>\n

In machine learning, samples represent all variables in the dataset and are divided into training, validation, and test samples.<\/p>\n

A sample contains one or more features and possibly a label. Samples can be labeled or unlabeled.

<\/p>\n

Labeled examples have a label that describes or characterizes the example, and unlabeled examples have no associated feature.<\/p>\n

The samples are the rows of the data matrix.<\/p>\n

Let $p$ and $q$ be the number of rows and columns in the data matrix.<\/p>\n

A sample is a vector $u \\in {R}^{q}$. In this regard, the data matrix contains $p$ samples,<\/p>\n

\\begin{eqnarray}
u_{i}:=row_{i}(d), \\quad i=1,\\ldots,p.
\\end{eqnarray}<\/p>\n

Designing a neural network to memorize a data set is not helpful.<\/p>\n

Instead, we want the neural network to perform accurately on new data, that is, to generalize.<\/p>\n

To achieve that, we divide the data set into three different subsets:<\/p>\n