How to Create an Ensemble of Machine Learning Models

This article describes how to create an ensemble of multiple Machine Learning and Regression models as shown below.

Requirements

At least two Machine Learning models - for Method 1 only.
Outcome variable to be predicted - for Method 2 only. If the outcome being predicted is numeric, the ensemble predicts the average model prediction of each case. If the outcome is categorical, the ensemble calculates the average probability of each class for each case and predicts the greatest probability.
Predictors variables will be considered as predictors of the outcome variable (eg, Age, Gender, Excercise frequency) - for Method 2 only.

From the Report tree select + > Advanced Analysis > Machine Learning > Ensemble of Models.
In the object inspector > Data > Existing Models > Input Models select the models you wish to compare.
OPTIONAL: Select Optimum Ensemble to find the ensemble with the best performance.
OPTIONAL: select the desired Output type:
- Comparison: A table comparing metrics for the models (and the ensemble(s), if selected).
- Ensemble: A Prediction-Accuracy Table for the ensemble (Optimal ensemble if selected) using the training data.

From the Report tree select + > Advanced Analysis > Machine Learning > Ensemble of Models.
In the object inspector go to Data > Existing or new models, select New models.
In Common Inputs > Outcome 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 Missing data treatment. (See Missing Data Options).
OPTIONAL: Select Variable names to display variable names in the output instead of labels.
OPTIONAL: Select Optimum Ensemble to find the ensemble with the best performance.
Go to Data > Model 1 > Algorithm and select the desired algorithm and settings.
Go to Data > Model 2 > Algorithm and select the desired algorithm and settings.
OPTIONAL: Add additional models.