mql5

Linear discriminant analysis (LDA) is a very common dimensionality reduction technique for classification problems. Like kohonen maps in prior article if you have high-dimensional data (i.e. with a large number of attributes or variables) from which you wish to classify observations, LDA will help you transform your data so as to make the classes as distinct as possible.

LDA is very much like the techniques PCA, QDA, & ANOVA; and the fact that they are all usually abbreviated is not very helpful. This article isn’t going to introduce or explain these various techniques, but simply highlight their differences.

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