Unlabeled data
Unlabeled data
Unlabeled data definition
Unlabeled data refers to a dataset that does not have any predefined categories or classifications. It is often used in machine learning and data mining applications to train models to identify patterns or make predictions based on the data’s inherent structure or characteristics.
See also: data backup
How is unlabeled data used:
- Semi-supervised learning. Used in conjunction with a small part of labeled data, it can train machine learning models.
- Clustering. By helping identify patterns and similarities in the data, it can help group them based on their shared characteristics.
- Anomaly detection. It can help identify anomalies, find data points that do not fit into a set pattern.
- Natural language processing. It can help identify topics and categories in text data used to classify new documents based on their content.
- Transfer learning. Transfer learning involves pre-training a model on a large dataset of unlabeled data, then fine-tuning the model on a smaller labeled dataset for a specific task. The pre-trained model can help the model learn more efficiently and effectively from the labeled data.
- Data augmentation: In data augmentation, it can be used to generate additional training data and help increase the size of the training set.