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.
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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.
Please note these steps require a Displayr license.
Method 1 - compare existing models
- In the Anything menu select Advanced Analysis > Machine Learning > Compare Models.
- In the object inspector > Data > 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 Data > Compare Machine Learning Models > Existing or new models, select New models.
- In the COMMON INPUTS > Output menu select the Outcome variable to be predicted by the predictor variables.
- Select the predictor variable(s) from the Predictor(s) list.
- 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.
Next
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How to Save Machine Learning Discrimination Variables
How to Save Machine Learning Predicted Values Variables
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