## Introduction

This article describes how to create a Machine Learning Linear Discriminant Analysis output as shown below. The table below shows the Mean output of a *linear discriminant analysis* predicting brand preference based on the attributes of the Coke brand.

## Requirements

- A categorical
**Outcome**variable to be predicted. - At least two
**Predictor**variables that will be considered as predictors of the outcome variable.

## Method

- In the
**Anything**menu select**Advanced Analysis > Machine Learning > Linear Discriminant Analysis.** - In the
**object inspector**go to the**Inputs**tab. - In the
**Outcome**select the variable to be predicted*.* - Select the predictor variable(s) in the
**Predictor(s)**section. - OPTIONAL: Select the desired
**Output**type:**Means**: Produces a table showing the means by category, and assorted statistics to evaluate the LDA - as shown above.**Detail**: More detailed diagnostics, from the`lda`function in the R`MASS`package.**Prediction-Accuracy Table**: Produces a table relating the observed and predicted*outcome*. Also known as a confusion matrix.**Scatterplot**: A two-dimensional scatterplot of the group centroids in the space of the first two discriminant function variables.**Moonplot**: A two-dimensional moonplot, using the same assumptions as the scatterplot.

- OPTIONAL: Select the desired
**Missing Data**treatment. (See Missing Data Options). - OPTIONAL: Select
**Variable names**to display variable names in the output instead of labels. - OPTIONAL: Select the desired
**Prior**probabilities to be used in case of computing the probabilities of the group membership of the**Outcome**. You can choose between:**Equal**: The prior probabilities are assumed to be equal for each**Outcome**group.**Observed**: Prior computed based on the current (weighted) group sizes. This is the default.

## See Also

How to Create a Classification And Regression Trees (CART)

How to Run Machine Learning Diagnostics - Prediction-Accuracy Table

How to Run Machine Learning Diagnostics - Table of Discriminant Function Coefficients extension

How to Create an Ensemble of Machine Learning Models

How to Run a Gradient Boosting Machine Learning Model

How to Compare Machine Learning Models

How to Save Machine Learning Discrimination Variables

How to Save Machine Learning Predicted Values Variables

How to Save Machine Learning Probability of Each Response Variable

How to Run Support Vector Machine

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