Specifying Drillthrough
Drillthrough can be enabled on models and on structures. The checkbox in this dialog box enables drillthrough on the named model. After the model has been processed, you will be able to retrieve detailed information from the training data that were used to create the model.
If the underlying mining structure has also been configured to allow drillthrough, you can retrieve detailed information from both the model cases and the mining structure, including columns that were not included in the mining model. For more information, see Using Drillthrough on Mining Models and Mining Structures (Analysis Services - Data Mining).
To name the model and structure and specify drillthrough
1.On the Completing the Wizard page, in Mining structure name, type Targeted Mailing.
2.In Mining model name, type TM_Decision_Tree.
3.Select the Allow drill through check box.
4.Review the Preview pane. Notice that only those columns selected as Key, Input or Predictable are shown. The other columns you selected (e.g., AddressLine1) are not used for building the model but will be available in the underlying structure, and can be queried after the model is processed and deployed.
5.Click Finish.
Previous Task in Lesson
Specifying the Columns used in the Mining Structure (Basic Data Mining Tutorial)
Next Lesson
Lesson 3: Adding and Processing Models
See Also
How to: Enable Drillthrough for a Mining Model
Using Drillthrough on Mining Models and Mining Structures (Analysis Services - Data Mining)
Specify the Training Data (Data Mining Wizard)
Lesson 3: Adding and Processing Models
The mining structure that you created in the previous lesson contains a single mining model that is based on the Microsoft Decision Trees algorithm. You can use this model to identify customers for the targeted mailing campaign. However, to ensure that your analysis is thorough, it is a common practice to create related models using different algorithms and compare their results. That way you can get different insights as well. Therefore, you will create two additional models, then process and deploy the models.
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In this lesson, you will create a set of mining models that will suggest the most likely customers from a list of potential customers.
To complete the tasks in this lesson, you will use the Microsoft Clustering Algorithm and the Microsoft Naive Bayes Algorithm.
This lesson contains the following tasks:
Adding New Models to the Targeted Mailing Structure (Basic Data Mining Tutorial) Processing Models in the Targeted Mailing Structure (Baisc Data Mining Tutorial)
First Task in Lesson
Adding New Models to the Targeted Mailing Structure (Basic Data Mining Tutorial)
Previous Lesson
Lesson 2: Building a Targeted Mailing Scenario (Basic Data Mining Tutorial)
Next Lesson
Lesson 4: Exploring the Targeted Mailing Models (Basic Data Mining Tutorial)
See Also
Adding Mining Models to a Structure (Analysis Services - Data Mining)
Adding New Models to the Targeted Mailing Structure (Basic Data Mining Tutorial)
In this task, you will define two additional models by using the Mining Models tab of Data Mining Designer. You will use the Microsoft Clustering and Microsoft Naive Bayes algorithms to create the models. These two algorithms are selected because of their ability to predict a discrete value (i.e., bike purchase). For more information about these algorithms, see Microsoft Clustering Algorithm (Analysis ServicesData Mining) and Microsoft Naive Bayes Algorithm
To create a clustering mining model
1.Switch to the Mining Models tab in Data Mining Designer in SQL Server Data Tools (SSDT).
Notice that the designer displays two columns, one for the mining structure and one for the TM_Decision_Tree mining model, which you created in the previous lesson.
2.Right-click the Structure column and select New Mining Model.
3.In the New Mining Model dialog box, in Model name, type TM_Clustering.
4.In Algorithm name, select Microsoft Clustering.
5.Click .
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The new model now appears in the Mining Models tab of Data Mining Designer. This model, built with the Microsoft Clustering algorithm, groups customers with similar characteristics into clusters and predicts bike buying for each cluster. Although you can modify the column usage and properties for the new model, no changes to the TM_Clustering model are necessary for this tutorial.
To create a Naive Bayes mining model
1.In the Mining Models tab of Data Mining Designer, right-click the Structure column, and select New Mining Model.
2.In the New Mining Model dialog box, under Model name, type
TM_NaiveBayes.
3.In Algorithm name, select Microsoft Naive Bayes, then click OK.
A message appears stating that the Microsoft Naive Bayes algorithm does not support the Age and Yearly Income columns, which are continuous.
4.Click Yes to acknowledge the message and continue.
A new model appears in the Mining Models tab of Data Mining Designer. Although you can modify the column usage and properties for all the models in this tab, no changes to the TM_NaiveBayes model are necessary for this tutorial.
Next Task in Lesson
Processing Models in the Targeted Mailing Structure (Baisc Data Mining Tutorial)
See Also
Adding Mining Models to a Structure (Analysis Services - Data Mining) Exploring the Targeted Mailing Models (Data Mining Tutorial) Managing Mining Models in Data Mining Designer
Processing Models in the Targeted Mailing Structure (Basic Data Mining Tutorial)
Before you can browse or work with the mining models that you have created, you must deploy the Analysis Services project and process the mining structure and mining models. Deploying sends the project to a server and creates any objects in that project on the server. Processing is the step, or series of steps, that populates Analysis Services objects with data from relational data sources. Models cannot be used until they have been deployed and processed.
Ensuring Consistency with HoldoutSeed
When you deploy a project and process the structure and models, individual rows in your data structure are randomly assigned to the training and testing set based on a random number seed. Typically, the random number seed is computed based on attributes of the data structure. For the purposes of this tutorial, in order to ensure that your results are
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the same as described here, we will arbitrarily assign a fixed holdout seed of 12. The holdout seed is used to initialize random sampling and ensures that the data is partitioned in roughly the same way for all mining structures and their models.
This value does not affect the number of cases in the training set; instead, it ensures that the partition can be repeated.
For more information on holdout seed, see Partitioning Data into Training and Testing Sets (Analysis Services - Data Mining).
To set the Holdout Seed
1.Click on the Mining Structure tab or the Mining Models tab in Data Mining Designer in SQL Server Data Tools (SSDT).
Targeted Mailing MiningStructure displays in the Properties pane.
2.Ensure that the Properties pane is open by pressing F4.
3.Ensure that CacheMode is set to KeepTrainingCases.
4.Enter 12 for HoldoutSeed.
Deploying and Processing the Models
In Data Mining Designer, you can process a mining structure, a specific mining model that is associated with a mining structure, or the structure and all the models that are associated with that structure. For this task, we will process the structure and all the models at the same time.
To deploy the project and process all the mining models
1.In the Mining Model menu, select Process Mining Structure and All Models.
If you made changes to the structure, you will be prompted to build and deploy the project before processing the models. Click Yes.
2.Click Run in the Processing Mining Structure - Targeted Mailing dialog box.
The Process Progress dialog box opens to display the details of model processing. Model processing might take some time, depending on your computer.
3.Click Close in the Process Progress dialog box after the models have completed processing.
4.Click Close in the Processing Mining Structure - <structure> dialog box.
There are multiple ways to process a model and structure. For more information, see the following topics:
•How to: Process a Mining Model
•How to: Process a mining structure
Previous Task in Lesson
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