Dimensionality Reduction
Techniques used to reduce the number of variables under consideration, often used in data mining to simplify models and avoid overfitting.
Implications
A process used in data analysis to reduce the number of variables under consideration by transforming data into a lower-dimensional space, often to simplify models, reduce noise, and prevent overfitting, crucial in machine learning and data mining.
Example
Example: A data scientist uses principal component analysis (PCA) to perform dimensionality reduction on a dataset with hundreds of variables, reducing it to a handful of key factors that explain most of the variance.
Related Terms
Different from feature selection, which involves choosing the most important variables, dimensionality reduction transforms the data into a lower-dimensional form while preserving important information.
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