A model is a reusable component created by training an algorithm using historical
data and saving the instance.
To create a model, you need to save the state of the algorithm. Typically, you create models
for the following reasons:
- To share computed business rules that can be applied to similar data
- To predict unseen data using the trained instance of the algorithm
- Acquire data from the required data source.
The data source component is added to a new analysis in the
Predict room.
- In the Predict room, double-click the required algorithm component.
- From the context menu for the component, choose Configure Settings
and configure the component settings.
- Choose
(Run
Analysis).
- From the context menu for the algorithm, choose Save as
Model.
- Enter a name and description for the model.
- If a model with the same name already exists, select the Overwrite,
if exists option to overwrite the existing model.
- Choose Save.
- Choose OK.
The model is created and appears in the
Models section under the
list of components on the right. You can use this model just like any other component
for creating an analysis.
Note Independent column names used while scoring the model
should be the same as the independent column names used while creating the model.