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Machine Learning
A dimensionality reduction technique that transforms data into a new coordinate system of uncorrelated variables.
Detailed Explanation
Principal Component Analysis (PCA) is a technique in machine learning used to reduce the number of features in a dataset while preserving as much variability as possible. It transforms the original correlated variables into a set of uncorrelated variables called principal components, ordered by the amount of variance they capture. This simplifies data analysis and helps visualize high-dimensional data effectively.
Use Cases
- •Use case: Reducing feature dimensions for visualization and noise reduction in high-dimensional datasets.
Related Terms
Other terms in the Machine Learning category