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