Example: Difference between revisions

Example (view source)

Revision as of 17:00, 19 February 2023

66 bytes removed , 19 February 2023

no edit summary

Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators

7,785

edits

@@ Line 1: / Line 1: @@
 ==Introduction==
-Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms that can learn from data and make predictions or decisions without being explicitly programmed. An important component of machine learning is the use of examples or training data, which consists of input data and the corresponding desired output. In this article, we will explain what an example is in machine learning and how it is used to train machine learning models.
+In [[machine learning]], [[example]]s or [[training data]] are [[input]] data and the corresponding desired [[output]]. A single [[example]] is the values of one row of [[features]] and possibly a label. Examples in [[supervised learning]] fall into two general categories: labeled examples and unlabeled examples. Labeled examples comprise one or more [[features]] and a [[label]]. Labeled examples are used during [[training]]. On the other hand, unlabeled examples consist of features but no label. Unlabeled examples can be utilized during training and [[inference]].
 ==What is an example in machine learning?==
-An example in machine learning is a pair of input and output values that is used to train a machine learning model. The input value is a set of features or attributes that describe the characteristics of an object or phenomenon, and the output value is the label or class that the object or phenomenon belongs to. For example, in a spam detection system, an example would consist of an email message (input) and its corresponding label (output), which could be either "spam" or "not spam". In an image recognition system, an example would consist of an image (input) and its corresponding label (output), which could be the name of the object or scene depicted in the image.
+An example in machine learning refers to a pair of input and output values used to train a model. The input value is made up of [[features]] or attributes that describe an object or phenomenon, while the output value serves as its [[label]] or [[class]]. For instance, spam detection systems typically take email messages (input) as their label (output), which could either be "spam" or "not spam." Similarly, image recognition systems take pictures as inputs and assign them labels describing what the image depicts.
 ==How are examples used in machine learning?==