Linear Discriminant Analysis for Machine Learning

Last Updated on August 15, 2020

Logistic regression is a classification algorithm traditionally limited to only two-class classification problems.

If you have more than two classes then Linear Discriminant Analysis is the preferred linear classification technique.

In this post you will discover the Linear Discriminant Analysis (LDA) algorithm for classification predictive modeling problems. After reading this post you will know:

• The limitations of logistic regression and the need for linear discriminant analysis.
• The representation of the model that is learned from data and can be saved to file.
• How the model is estimated from your data.
• How to make predictions from a learned LDA model.
• How to prepare your data to get the most from the LDA model.

This post is intended for developers interested in applied machine learning, how the models work and how to use them well. As such no background in statistics or linear algebra is required, although it does help if you know about the mean and variance of a distribution.

LDA is a simple model in both preparation and application. There is some interesting statistics behind how the model is setup and how the prediction equation is derived, but is not
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