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	<title>Machine learning terminology - Revision history</title>
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	<updated>2026-04-05T04:01:21Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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		<id>https://machinelearning.subwiki.org/w/index.php?title=Machine_learning_terminology&amp;diff=97&amp;oldid=prev</id>
		<title>Vipul: Created page with &quot;This page lists standard terminology in machine learning and corresponding terminology we might see in other related areas.  {| class=&quot;sortable&quot; border=&quot;1&quot; ! Most standard ter...&quot;</title>
		<link rel="alternate" type="text/html" href="https://machinelearning.subwiki.org/w/index.php?title=Machine_learning_terminology&amp;diff=97&amp;oldid=prev"/>
		<updated>2014-08-15T18:21:20Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;This page lists standard terminology in machine learning and corresponding terminology we might see in other related areas.  {| class=&amp;quot;sortable&amp;quot; border=&amp;quot;1&amp;quot; ! Most standard ter...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;This page lists standard terminology in machine learning and corresponding terminology we might see in other related areas.&lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;sortable&amp;quot; border=&amp;quot;1&amp;quot;&lt;br /&gt;
! Most standard term !! Other terms that may be used !! Meaning&lt;br /&gt;
|-&lt;br /&gt;
| Example || Data point, instance, case, specific feature values, specific input values || The machine learning problem generally involves predicting an output (label) from input information (i.e., the values of particular features). Each particular choice of input values (feature values) constitutes one example. The example is called &amp;#039;&amp;#039;labeled&amp;#039;&amp;#039; if the corresponding observed output is included, and &amp;#039;&amp;#039;unlabeled&amp;#039;&amp;#039; otherwise.&lt;br /&gt;
|-&lt;br /&gt;
| Label || Output, observed value, specific output value || For any individual example, the corresponding label is the output as actually observed.&lt;br /&gt;
|-&lt;br /&gt;
| Model || Function, Predictive model || This is the function that we are trying to find, that does a good job of predicting the output from the inputs (features).&lt;br /&gt;
|-&lt;br /&gt;
| [[Feature]] || Input coordinate, spanning function, (corresponds to) parameter || For each example, specifying the example involves specifying the values of all features. Each feature corresponds to one coordinate of the input domain. We can distinguish between &amp;quot;elementary features&amp;quot; that need to be explicitly specified, and &amp;quot;derived features&amp;quot; that can be deduced from the elementary features.&lt;br /&gt;
|-&lt;br /&gt;
| Model type || Functional form || This is the general type of function that we are trying to fit, with potentially unknown parameters, that are to be determined using the learning algorithm.&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Vipul</name></author>
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