Compare the performance of multiple Machine Learning and Regression models by producing a table of metrics from each model. The metrics are computed based on each model's training data. Optionally, a filter specifying evaluation data (usually a testing sample independent of the training sample) may also be provided. The models may either be existing already or created for the comparison.
For information on how to compare machine models in Displayr, see How to Compare Machine Models.
Example
Options
Existing or new models Choose to use existing machine learning models or create new models to compare.
Existing models
Input models At least 2 existing machine learning models.
Ensemble Whether to create an ensemble model by combining the predictions of the underlying 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.
Random seed Seed used to initialize the (pseudo) random number generator for the model fitting algorithm.
Ensemble Whether to create an ensemble model by combining the predictions of the underlying models.
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. See for more details.
For model-specific options see Classification And Regression Trees (CART), Linear Discriminant Analysis, Random Forest, Support Vector Machine, Deep Learning, Gradient Boosting or Regression.