Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
In-depth explanation
Unsupervised learning algorithms discover hidden structures in data without any guidance about what to look for. Common techniques include clustering (grouping similar items), dimensionality reduction (simplifying data), and anomaly detection (finding outliers). This approach is valuable when labeled data is unavailable or expensive to obtain.
Examples
More in Machine Learning
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
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