Adding a Logistic Regression Model to the Call Center Structure (Intermediate Data Mining Tutorial)
In addition to analyzing the factors that might affect call center operations, you were also asked to provide some specific recommendations on how the staff can improve the service grades. In this task, you will use the same mining structure that you used to build the exploratory model and add a mining model that will be used for creating predictions.
In Analysis Services, a logistic regression model is based on the neural networks algorithm, and therefore provides the same flexibility and power as a neural network model, but is more suited to predicting outcomes.
For this scenario, you can re-use all the columns that were included in the neural network model. However, when you add new models to a mining structure, by default the new models are created with the same inputs and predictable attributes as the first mining model. Therefore, you must change the inputs and predictable attributes to customize the new model for your business questions.
Also to ensure that the call center models are as similar as possible, you will set the seed parameter for both models. . The seed parameter ensures that a model stays the same throughout reprocessing. If you do not specify a numeric value for the seed parameter, SQL Server Analysis Services will generate a seed based on the name of the model. Because the neural network model and the logistic regression model that you are building all have different names, you must set a seed value to ensure that they process the data starting at the exact same point.
To add a new mining model to the call center mining structure
In Business Intelligence Development Studio, in Solution Explorer, right-click the mining structure, Call Center Binned, and select Open Designer.
In Data Mining Designer, click the Mining Models tab.
Click Create a related mining model.
In the New Mining Model dialog box, for Model name, type Call Center - LR. For Algorithm name, select Microsoft Logistic Regression.
Click OK.
The new mining model is displayed in the Mining Models tab.
To customize the logistic regression model
In the column for the new mining model, Call Center - LR, leave Fact CallCenter ID as the key.
Change the value of ServiceGrade and Level Two Operators to Predict.
These columns will be used both as input and for prediction. When you include multiple predictable attributes in a neural network model or logistic regression model, in essence you are creating two separate models within the same metadata container. The algorithm creates a separate subtree for each set predictable attributes.
Change all other columns to Input.
To specify the seed and process the models
In the Mining Model tab, right-click the column for the model named Call Center - LR, and select Set Algorithm Parameters.
In the row for the HOLDOUT_SEED parameter, click the empty cell under Value, and type 1. Click OK.
Note
The value that you choose as the seed does not matter, as long as you use the same seed for all related models.
In the Mining Models menu, select Process Mining Structure and All Models. Click Yes to deploy the updated data mining project to the server.
In the Process Mining Model dialog box, click Run.
Click Close to close the Process Progress dialog box, and then click Close again in the Process Mining Model dialog box.