Classification algorithms

Learn Linear Discriminant Analysis with R: A Step-by-Step Tutorial

Linear Discriminant Analysis (LDA) is a foundational statistical technique used extensively in machine learning for both supervised classification and effective dimensionality reduction. Its primary goal is to find linear combinations of features that best separate two or more classes of objects. Unlike Principal Component Analysis (PCA), which focuses on maximizing variance, LDA specifically seeks to […]

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Learning Quadratic Discriminant Analysis (QDA) with R: A Step-by-Step Guide

Quadratic Discriminant Analysis (QDA) stands as a sophisticated statistical method essential for classification tasks. Its primary function is to predict a categorical response variable utilizing a collection of continuous or discrete predictor variables. A core assumption of QDA is that observations within each specified class are derived from a Gaussian distribution. Crucially, QDA distinguishes itself

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