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Curse of Dimensionality
Challenges arising when working with high-dimensional data, including data sparsity and computational complexity.
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- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Feature ✓ understood
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
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Foundations Curse of Dimensionality Phenomena where algorithms become inefficient as dimensionality increases, including data sparsity and distance concentration. Foundations Dimensionality Reduction Techniques to reduce the number of input features while preserving important information (PCA, t-SNE, autoencoders). Foundations Feature Selection Choosing the most relevant features from available data to reduce dimensionality and improve model performance. Foundations Principal Component Analysis A dimensionality reduction technique that transforms data into orthogonal components ordered by variance explained.