The data set being scored must contain all features used during training, missing feature(s)

Question

I built a model in Azure ML, then i published this model as a web service. I would like to customize the input fields of the web service by updating the needed inputs for the model prediction WS.

The model has been trained on set of features to predict a price value on a given date. I want the customer to provide a date to predict the price without the need to enter features values that i supplied when i trained the model.

The error message when i customize the web service inputs by removing the unneeded columns in the predictive experiment (by adding select column module before the score module):

Error 1000:
AFx Library library exception: table: The data set being scored must contain all features used during training, missing feature(s).

How would i fix this issue?

Answer 1

I had this same problem, with the below error.

AFx Library library exception: table: The data set being scored must contain all features used during training, missing feature(s).

This happened when i changed my classification algorithm to regression algorithm in the same project. I got it cleared by creating a new project with same steps and all worked perfectly fine.

I think the problem is when we change the type of algorithm, ML studio is confused.

Answer 2

The Score Model module needs the same input features that were used to train the model. That's a basic property of the machine learning algorithms.

Could you clarify where the feature value come from, if not from customer?

-Roope