How to Run Support Vector Machine

This article describes how to create a Suppor Vector Machine output as shown below.

The table below shows the Accuracy as computed by a Support Vector Machine. The Overall Accuracy is the percentage of instances that are correctly categorized by the model. The accuracies of each individual class are also displayed.

Requirements

• A numeric or categorical variable to be used as an Outcome variable to be predicted.
• Predictors variables will be considered as predictors of the outcome variable.

Method

1. In the Anything menu select Advanced Analysis > Machine Learning > Support Vector Machine.  
2. In the object inspector go to the Inputs tab.
3. In the Output menu select the variable to be predicted by the predictor variables. 
4. Select the predictor variable(s) from the Predictor(s) list.
5. OPTIONAL: Select the desired Output type:
   • Accuracy: Produces measures of the goodness of model fit, as illustrated above.
   • Prediction-Accuracy Table: Produces a table relating the observed and predicted outcome. Also known as a confusion matrix.
   • Detail: This returns the default output from svm in the e1071 package.
6. OPTIONAL: Select the desired Missing Data treatment. (See Missing Data Options).
7. OPTIONAL: Select Variable names to display variable names in the output instead of labels.
8. OPTIONAL: Input the desired Cost value. Cost controls the extent to which the model correctly predicts the outcome for each training example.

See Also

How to Create a Classification And Regression Trees (CART)

How to Run Deep Learning

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 Run Machine Learning Linear Discriminant Analysis

How to Run Mixed-Mode Tree

How to Run Random Forest

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