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| ==en construction==
| | #REDIRECTION [[Analyse discriminante linéaire]] |
| Voir Analyse discriminante linéaire
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| == Définition ==
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| == Français ==
| | [[Catégorie:ENGLISH]] |
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| == Anglais ==
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| ''' Linear Discriminant Analysis '''
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| '''LDA'''
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| PCA and LDA are two data preprocessing linear transformation techniques that are often used for dimensionality reduction to select relevant features that can be used in the final machine learning algorithm. PCA is an unsupervised algorithm that is used for feature extraction in high-dimensional and correlated data. PCA achieves dimensionality reduction by transforming features into orthogonal component axes of maximum variance in a dataset. The goal of LDA is to find the feature subspace that optimizes class separability and reduce dimensionality (see figure below). Hence, LDA is a supervised algorithm.
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| [https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html Source : kdnuggets]
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| [[Catégorie:vocabulary]] | |
