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.
- 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.
- In the Anything menu select Advanced Analysis > Machine Learning > Support Vector Machine.
- In the object inspector go to the Inputs tab.
- In the Output menu select the variable to be predicted by the predictor variables.
- Select the predictor variable(s) from the Predictor(s) list.
- 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.
- 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: Input the desired Cost value. Cost controls the extent to which the model correctly predicts the outcome for each training example.