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- Linear Discriminant Analysis in Machine Learning
Linear Discriminant Analysis (LDA) also known as Normal Discriminant Analysis is supervised classification problem that helps separate two or more classes by converting higher-dimensional data space into a lower-dimensional space
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- Linear Discriminant Analysis (LDA) — STATS 202
Linear Discriminant Analysis (LDA) Strategy: Instead of estimating P (Y ∣ X) directly, we could estimate: P ^ (X ∣ Y): Given the response, what is the distribution of the inputs P ^ (Y): How likely are each of the categories Then, we use Bayes rule to obtain the estimate:
- What is linear discriminant analysis (LDA)? - IBM
Linear discriminant analysis (LDA) is an approach used in supervised machine learning to solve multi-class classification problems
- Linear Discriminant Analysis (LDA) - Machine Learning Explained
Linear Discriminant Analysis (LDA) is a dimensionality reduction technique commonly used for supervised classification problems The goal of LDA is to project the dataset onto a lower-dimensional space while maximizing the class separability
- Introduction to Linear Discriminant Analysis - Statology
However, when a response variable has more than two possible classes then we typically prefer to use a method known as linear discriminant analysis, often referred to as LDA For example, we may use LDA in the following scenario:
- LDA in Machine Learning - Tpoint Tech - Java
Linear Discriminant Analysis (LDA) is one of the commonly used dimensionality reduction techniques in machine learning to solve more than two-class classification problems It is also known as Normal Discriminant Analysis (NDA) or Discriminant Function Analysis (DFA)
- Linear Discriminant Analysis - A Comprehensive Guide
The idea behind Linear Discriminant Analysis (LDA) is to dimensionally reduce the input feature matrix while preserving as much class-discriminatoryinformation as possible LDA tries to express the dependent variable as a linear combination of other features
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