Introduction to the SQL Server Analysis Services Logistic Regression Data Mining Algorithm

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How can I set up a project to use the SQL Server Analysis Services logistic regression data mining algorithm?


In this tip, we show how to create a simple data mining model using the Logistic Regression algorithm in SQL Server Analysis Services. The data set we will use is visualized below. We are trying to classify the false samples in red and the true samples in blue. This tip uses SQL Server 2014 Analysis Services and Visual Studio 2012.

Data used in this tip

The data is stored in the table whose structure is shown below. We have a primary key column, along with columns for our X and Y values. The last column is where we store the class label.

Table used in this tip

In Visual Studio, create a new Analysis Services Multidimensional and Data Mining Project.

New project

In this tip, we will name the project LogisticRegressionExample. Click on OK when finished with the New Project window.

New project naming

In the Solution Explorer window, right-click on the Data Sources folder and choose "New Data Source..." to initiate the Data Source Wizard.

Start new data source

Click on "Next >".

Data source wizard start page

Choose your data connection, if one exists. If a data connection does not exist, click on "New..." to create a new data connection.

Blank data connection

In this example, we are using a connection to the Tips database on the localhost.

New data connection window

Click on "Next >".

Completed data connection page

On the Impersonation Information screen, click on "Use a specific Windows user name and password." Enter your username and password. Click on "Next >".

Impersonation information

On the Completing the Wizard screen, the data source name can be changed if desired. Click on "Finish".

Completing the data source wizard page

The new data source will appear in the Solution Explorer.

New data source in Solution Explorer

In the Solution Explorer window, right-click on the Data Source Views folder and choose "New Data Source View..." to launch the Data Source View Wizard.

Launch new data source view

Click on "Next >".

First page of data source view wizard

On the Select a Data Source page in the Relational data sources window, select the data source we created in the above step. Click on "Next >".

Select a data source

On the Select Tables and Views page, move the table tblLogisticRegressionExample from the Available Objects box to the Included object box by selecting tblLogisticRegressionExample in the Available objects box and then clicking on the ">" box. Click on "Next >".

Select tables and views

On the Completing the Wizard page, give the Data Source View a name and click on "Finish".

Completing the data source view wizard

The data source view now appears in the Solution Explorer window.

Solution Explorer with the new data source view

Right-click on the Mining Structures folder and select "New Mining Structure..." to launch the Data Mining Wizard.

Launch the data mining wizard

Click on "Next >".

First page of the data mining wizard

Press the "From existing relational database or data warehouse" radio button and then click "Next >".

Use existing relational database

Select the Microsoft Logistic Regression as the data mining technique. Please note in the description that the "algorithm is a particular configuration of the Microsoft Neural Network algorithm." This will become important later when it is time to view the results.

Select the Microsoft Logistic Regression

On the Select Data Source View page, we will use our previously defined objects. Click on "Next >".

Select Data Source View

Next, check the Case box on the ColumnsForDataMining line. Click on "Next >".

Specify table types

On the Specify the Training Data page, check the box in the Key column that corresponds with the primary key column. The AttributeX and AttributeY columns will be used as input. The BinaryClass column is our class label, so we check the Predictable box for the BinaryClass column. Click on "Next >".

Specify training data

The default values are shown below on the Specify Columns' Content and Data Type page. The values displayed in the Content Type and Data Type columns accurately represent the data in the source table. In this case, there is no need to click the Detect button. Click on "Next >."

Specify Column Content and Data Type

We will use 30% of our data for testing the mining model's accuracy. Click on "Next >".

Create testing set

On the Completing the Wizard screen, we can rename the mining structure name and the mining model name. Click on "Finish".

Data mining wizard completed

Our mining structure now appears in the Solution Explorer.

Mining structure in Solution Explorer

The Mining Structure tab is selected by default. At this point the Analysis Services objects reside in the Visual Studio project and not on the server. Click on the Mining Model Viewer tab.

Mining Structure tab

Visual Studio will attempt to deploy the SSAS objects to the server specified in the project properties. When asked if "Would you like to build and deploy the project first?", choose "Yes".

Attempt to deploy objects

When given the warning about the time it could take to process the mining model and asked "Do you wish to continue?", choose "Yes". The number of records in the table is not a large amount, so it should not take more than a minute to process.

Processing time

When the Process Mining Model window appears, press the "Run..." button.

Process mining model window

The Process Progress window will appear. When the process completes successfully select "Close" in the Process Progress window and "Close" again in the Process Mining Model window.

Process progress window

Depending on your hardware configuration, the Load Mining Model Content window might appear stating to "Please wait...".

Load mining model content

The Deployment Progress window will appear also stating that the SSAS objects were successfully deployed to the Analysis Services server.

Deployment Progress window

In the Mining Model Viewer tab, we can see which attributes and their values favor the False classification and which attributes and their values favor the True classification. The wider the blue bar indicates the higher likelihood that a specific key-value pair will favor a particular classification. In the example shown here, when AttributeX is 0.684 to 1.000 the classification tends to be False. When AttributeX is between 0.000 to 0.298, then the classification tends to be True. The Logistic Regression algorithm utilizes the Microsoft Neural Network Viewer.

Mining Model Viewer tab

When we click on the Mining Accuracy Chart and then click on the Classification Matrix page, we can see the confusion matrix for the Logistic Regression algorithm. This displays the count of true positives, true negatives, false positives and false negatives in the 30% test population.

Mining Accuracy Chart

In this tip, we have provided an introduction to the Logistic Regression data mining algorithm in SQL Server 2014 Analysis Services.

Next Steps

Check out these other tips on data mining in SQL Server Analysis Services.

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About the author
MSSQLTips author Dallas Snider Dr. Dallas Snider is an Assistant Professor in the Computer Science Department at the University of West Florida and has 18+ years of SQL experience.

This author pledges the content of this article is based on professional experience and not AI generated.

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