Unsupervised learning
Unsupervised learning is a type of machine learning that focuses on finding patterns and structures in unlabeled data sets. Unlike supervised learning, where pre-labeled data is available, unsupervised learning has no access to labeled data. This means that the algorithm must train itself by looking for inherently present patterns and structures in the dataset.
How does unsupervised learning work?
Unsupervised learning uses algorithms to identify and highlight structure and patterns in unlabeled datasets. There are two main methods used in unsupervised learning: clustering and dimensionality reduction.
Clustering is the method in which algorithms divide the data set into different groups (clusters) based on the similarities between data points. These clusters are often based on features such as distance or similarity. Dimensionality reduction, on the other hand, focuses on reducing the complexity of the dataset by converting it to a lower dimension. This allows the dataset to be better understood and to identify patterns that were not evident in the original dataset. In general, unsupervised learning can be used to gain insights into the structure and patterns of datasets to help make decisions and identify trends.
