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{{see also|Machine learning terms}}
{{see also|Machine learning terms}}
==Introduction==
==Introduction==
Machine learning often uses labeled data for model training. Labeled data refers to information that has already been classified and labeled by humans, making it simpler for the model to comprehend and learn from. On occasion, however, unlabeled data may also be employed.
[[Unlabeled example]] has [[features]] but no [[label]]. In [[supervised learning]], [[labeled example]]s are used for [[training]] the model while unlabeled examples are used for prediction. In [[semi-supervised training|semi-supervised]] and [[unsupervised training]], unlabeled examples are used for training the model.
 
[[Machine learning]] often uses [[labeled data]] for [[model]] [[training]]. Labeled data refers to information that has already been [[classified]] and [[labeled]] by humans, making it simpler for the model to comprehend and learn from. On occasion, however, [[unlabeled data]] may also be employed.


==What is Unlabeled Data?==
==What is Unlabeled Data?==
Unlabeled data, as its name suggests, refers to data that has not been labeled or categorized in any way. It's essentially raw information that hasn't been preprocessed or organized. For instance, if we were building a model to recognize different types of fruit, labeled data would be an array of images labeled with their associated type (e.g. apple, orange, banana) while unlabeled data consists solely of images without labels attached.
Unlabeled data, as its name suggests, refers to [[data]] that has not been labeled or categorized in any way. It's essentially raw information that hasn't been [[preprocess]]ed or organized. For instance, if we were building a model to recognize different types of fruit, labeled data would be an array of images labeled with their associated type (e.g. apple, orange, banana) while unlabeled data consists solely of images without labels attached.


==Why Use Unlabeled Data?==
==Why Use Unlabeled Data?==