Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators
7,785
edits
Unlabeled example: Difference between revisions
m
Text replacement - "Category:Machine learning terms" to "Category:Machine learning terms Category:not updated"
No edit summary |
m (Text replacement - "Category:Machine learning terms" to "Category:Machine learning terms Category:not updated") |
||
| (One intermediate revision by the same user not shown) | |||
| Line 1: | Line 1: | ||
{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
[[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. | [[Unlabeled example]] or '''unlabled data''' 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. | [[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. | ||
| Line 24: | Line 24: | ||
==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 [[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. | 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?== | ||
At first glance, using unlabeled data for training | At first glance, using unlabeled data for [[training model]]s may seem counterintuitive since it lacks the organized structure and organization that labeled data provides. However, there are numerous reasons why unlabeled data can be beneficial in machine learning applications. | ||
Firstly, unlabeled data is typically more abundant than labeled data. Collecting and labeling data can be a time-consuming and expensive process, particularly for large datasets. By using unlabeled data instead of labeling it ourselves, we can increase the size of our training set without incurring additional expenses associated with labeling. | Firstly, unlabeled data is typically more abundant than labeled data. Collecting and labeling data can be a time-consuming and expensive process, particularly for large datasets. By using unlabeled data instead of labeling it ourselves, we can increase the size of our training set without incurring additional expenses associated with labeling. | ||
Second, unlabeled data can be utilized to enhance the accuracy and robustness of models. A common technique is semi-supervised | Second, unlabeled data can be utilized to enhance the [[accuracy]] and robustness of models. A common technique is semi-supervised training, where a model is trained on both labeled and unlabeled data. By using the unlabeled data to detect patterns and structure within it, the model can make better predictions when dealing with labeled information. | ||
Finally, unlabeled data can be employed for unsupervised learning, which seeks to detect patterns and | Finally, unlabeled data can be employed for unsupervised learning, which seeks to detect patterns and structures without any prior knowledge or guidance. This approach has applications such as anomaly detection and clustering. | ||
==How is Unlabeled Data Used?== | ==How is Unlabeled Data Used?== | ||
Training models on unlabeled data requires several techniques. Clustering is one popular approach, where similarity between data points causes the data points to be grouped together in clusters. This can be helpful for uncovering patterns and relationships within the dataset. | Training models on unlabeled data requires several techniques. [[Clustering]] is one popular approach, where the similarity between data points causes the data points to be grouped together in clusters. This can be helpful for uncovering patterns and relationships within the dataset. | ||
Another technique is dimensionality reduction, where data is transformed into a lower-dimensional space while preserving its essential features. This can be helpful for visualizing the data and recognizing patterns that may not be visible in its original high-dimensional space. | Another technique is [[dimensionality reduction]], where data is transformed into a lower-dimensional space while preserving its essential features. This can be helpful for visualizing the data and recognizing patterns that may not be visible in its original high-dimensional space. | ||
[[Autoencoder]]s are a type of neural network that can be trained on unlabeled data to produce a [[compressed representation]]. This type of representation could be helpful for tasks such as [[image generation|image]] or [[text generation]]. | |||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
| Line 75: | Line 75: | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||