Our web service is almost complete. However we need to tune the logic behind the web service function. The score model module is the module that will execute the algorithm against a given dataset. The score model module can also be called the “prediction module” because that is what happens when you apply a trained algorithm against a dataset.
You will notice that the score model module also takes in a dataset on the right input node. When deploying a predictive model, the score model module will need a copy of the required schema. The dataset used to train the model is fed back into the score model module because that is the schema that our trained algorithm currently knows.
However, that schema also holds our response class “survived,” the attribute that we are trying to predict. We must now drop the survived column. To do this we will use the “project columns” module. Search for it in the search bar on the left side of the AzureML window, then drag it into the workspace.
Replicate the picture on the left by connecting the last metadata editor’s output node to the input of the new project columns module. Then connect the output of the new project columns module with the right input of the score model module.
Select the project columns module once the connections have been made. A “properties” window will appear on the right side of the AzureML window. Click on “launch column selector.”
To drop the “Survived” column we will “Begin with: All Columns,” then choose to “Exclude” by “column names,” “Survived.”