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This task is task dependent AutoML. To use it, you must have previously created at least one model using AutoML.
Its usefulness is to apply the model to a new set of data. As an example, a company needs to charge its customers. To do this, she built a predictive model (AutoML) using her billing history, as she knows who paid and who didn't pay.
Now the company received a portfolio of customers at the beginning of the month to charge and wants to apply the model it had created to know who will pay and who will not pay. In addition to having this marking, she also wants to know the probability (0 to 1 or 0% to 100%) for each customer.
- 1.Set the task name
- 2.Define the name of the result table with the classifications
- 3.Choose the model that should be applied to the data
The table that contains the customer portfolio for billing must have exactly the same columns as those in the table used to create the model with AutoML. The only column it will not have is the response variable, which in practice is what we want to know now (which customer will pay or not). Therefore, in AutoML , it is important to only use variables that we will have later to apply Scoring . If the columns do not match, an error will be returned .
The result of executing the Scoring task will be a table, containing all the original columns of the table used, including the new scoring results columns. Below is an example for the case where the response variable is categorical and has two values ( no and yes ).
- 1.Forecast with two possible values for each customer.
- 2.Probability of no.
- 3.Probability of yes.
- 4.From that point on, all columns from the source table are repeated and are available for any analysis in the Gaio process.