Linear Discriminant Analysis (LDA) is a dimensionality reduction technique used in supervised learning to project data onto a lower-dimensional space while maximizing class separability. It is particularly effective for classification tasks where the goal is to find a linear combination of features that best separates two or more classes.

Linear Discriminant Analysis

dimensionality reduction technique

Supervised learning is a machine learning paradigm where a model is trained on a labeled dataset, meaning that each training example is paired with an output label. The goal is for the model to learn a mapping from inputs to outputs, enabling it to make accurate predictions on new, unseen data.

supervised learning

lower-dimensional space

Class separability refers to the degree to which different classes in a dataset can be distinguished from one another based on their feature values. It is a crucial factor in the performance of classification algorithms, as higher class separability generally leads to more accurate predictions.

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