Input Space
Unsupervised learning is a type of machine learning where the model is trained on a dataset without any supervision or labeled data. The goal of unsupervised learning is to find patterns and relationships in the data that can be used to gain insights or make predictions. Clustering and dimensionality reduction are common techniques used in unsupervised learning. Clustering involves grouping similar data points together, while dimensionality reduction involves reducing the number of features in the dataset while retaining as much information as possible. Unsupervised learning is often used in exploratory data analysis and can be useful in identifying anomalies or outliers in the data.
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