How to Compare Machine Learning Models

This article describes how to compare the performance of multiple Machine Learning and Regression models by producing a table of metrics from each model as shown below.

Requirements

At least two Machine Learning models - for Method 1 only.
Outcome variable to be predicted (eg, Preffeer Cola) - for Method 2 only.
Predictors variables will be considered as predictors of the outcome variable (eg, Age, Gender, Excercise frequency) - for Method 2 only.
Note: Predictors that are considered to be uninformative will be automatically excluded from the model.

Method 1 - compare existing models

In the Anything menu select Advanced Analysis > Machine Learning > Compare Models.
In the object inspector > Inputs > EXISTING MODELS > Inputs Models select the models you wish to compare.

Method 2 - compare new models

In the Anything menu select Advanced Analysis > Machine Learning > Compare Models.
In the object inspector go to Inputs > Compare Machine Learning Models > Existing or new models, select New models.
In the COMMON INPUTS > Output menu select the variable to be predicted by the predictor variables.
Select the predictor variable(s) from the Predictor(s) list.
Go to Inputs > MODEL 1 > Algorithm and select the desired algorithm and settings.
Go to Inputs > MODEL 2 > Algorithm and select the desired algorithm and settings.
OPTIONAL: add additional models.