Define Settings and Train a Classification or Regression Predictive Model

You enter values for parameters in the Settings tab. These are used to train a predictive model. Training is a process that takes these values and uses SAP machine learning algorithms to explore relationships in your data source to come up with the best combinations for the predictive model.

Enter new or change values for the following settings:

General section

The predictive model name is a default one and can't be edited, but you can add descriptive text if required.
Training Data source: Browse to and select a dataset that contains the historical data you want to use to train the predictive model. This dataset must already be available to SAP Analytics Cloud.
Edit Column Details: Click to open the list of columns in the dataset. Properties such as data type, statistical type, and others can be changed in the predictive model. This is for users that are familiar with the dataset. In general you won't have to consider this dialog unless you are trouble shooting. More information can be found here:Editing Column Details.

Predictive Goal section

Target: Browse to and select the variable that you want to predict values for.

Influencers section

Columns that can have an influence on the target values. Certain may have too much influence on the target, and so can cloud the effect of other columns that are related to your business question.
Exclude as Influencers: You can select the columns that you don't want to be taken into account when the predictive model is trained.
Limit Number of Influencers: You can specify the the maximum number of columns that the predictive model will consider as influencers. Only the most contributive influencers are retained.

Train: Click to start training the predictive model with your settings. There is also a Train Predictive Model icon in the toolbar.

What's next?

The training produces performance indicators that you will use to evaluate the results. This is called debriefing the predictive model.