Adding features and target to a Vital AI predictive model

Part of a series to introduce the Vital AI software used to make predictions.

Go to beginning: Using the Vital AI software to make predictions

We can edit our data model to define a predictive model.

For our predictive model, we define:

  • A unique identifier for the model (URI)
  • A name for the model
  • The features (inputs) to the model, including their datatype. In this example, the inputs will be textual.
  • The target (output) of the model, including its datatype. In this example, the output is categorical (one of a list of options).
  • The machine learning algorithm to use with the predictive model. In this case, we’ll use complementary bayes.

The predictive model is defined by a combination of individuals and annotations.


Features are specified, such as the “hasBody” property.


The Target property is specified:


We can also specify how results of the prediction will be asserted:


We can check the definition of the model using the “showmodels” option of the vitalpredict command.

vitalpredict showmodels


Now that we have our model defined, we can create our dataset.

Next: Creating a predictive model training set with Vital AI

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