Definition
Unlabeled data is a dataset that lacks any predetermined classifications or categories. It is usually utilized in data mining and machine learning applications to train models to discover patterns or make forecasts based on the data’s innate characteristics or structure.
How Unlabeled Data is Utilized
- Semi-supervised learning: When combined with a small segment of labelled data, unlabeled data can train machine learning models.
- Clustering: Identifying patterns and data similarities can assist in categorizing them based on their shared attributes.
- Anomaly detection: It can assist in anomaly identification and discovering data points that fail to fit into a set pattern.
- Natural language processing: It can help identify categories and subjects in text data, which is used to categorize new documents according to their content.
- Transfer learning: This process involves pre-training a model on a vast amount of unlabeled data and then refining the model on a smaller labelled dataset for a specific purpose. The pre-trained version may assist the model in learning successfully and efficiently from the labelled data.
- Data augmentation: Unlabeled data can be used in data augmentation to generate extra training data and help enhance the size of the training set.