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