Unlabeled example: Difference between revisions

[[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 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.

At first glance, using unlabeled data for training models 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.

Second, unlabeled data can be utilized to enhance the accuracy and robustness of models. A common technique is semi-supervised learning, 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 structure without any prior knowledge or guidance. This approach has applications such as anomaly detection and clustering.

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.

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.

Autoencoders 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 or text generation.