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
Related terms
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
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.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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