An ensemble of multiple Machine Learning and/or Regression models. The models may already exist or created for the ensemble.
Example
Comparison table for 3 models:
Options
Existing or new models - Choose to use existing machine learning models or create new models to compare.
Ensemble - Check the Ensemble checkbox to create an ensemble model by combining the predictions of the underlying models.
Optimal ensemble - Whether to find the ensemble with the best evaluation accuracy or R-squared (or training accuracy or R-squared if no evaluation filter is supplied).
Output
- 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.
Existing models
Input models - At least 2 existing machine learning models.
New models
Outcome - The variable to be predicted by the predictor variables.
Predictors - The variable(s) to predict the Outcome.
Missing data - See Missing Data Options.
Variable names - Displays variable names in the output instead of labels.
Random seed - Initializes the random number generator for imputation and algorithms with randomness.
Evaluation filter - Select a filter to apply to the models.
Models - For each model, select a machine learning algorithm and the desired settings for each model.
For model-specific options see: Classification And Regression Trees (CART), Linear Discriminant Analysis, Random Forest, Support Vector Machine, Deep Learning, Gradient Boosting, or Regression.
DIAGNOSTICS
Prediction-Accuracy Table - Creates a table showing the observed and predicted values, as a heatmap.
SAVE VARIABLE(S)
Predicted Values - Creates a new variable containing predicted values for each case in the data.
Probabilities of Each Response - Creates new variables containing predicted probabilities of each response.
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Displayr: How to Create an Enemble of Machine Learning Models
Q: Machine Learning Ensemble of Models