This document discusses building a predictive model using Microsoft Decision Trees to predict a prospect's likelihood of purchasing a bike. It outlines setting up the necessary software including SQL Server and Visual Studio, creating a data mining project with a data source and mining structure using Microsoft Decision Trees, deploying and processing the model, and analyzing the model's accuracy using holdout data and visualizations. The summary emphasizes that Microsoft Decision Trees is a powerful yet easy to use model that can perform both single and mass predictions, and that holdout data and visualizations help test and explore the trained model.

1. Data Mining By Example – Building
Predictive Model Using Microsoft
Decision Trees
by Shaoli Lu

2. Microsoft Decision Trees
• Developed by Microsoft research team, the
Microsoft Decision Trees algorithm is a hybrid
decision tree algorithm that supports
classification and regression

3. Goal
• To predict a prospect’s likelihood of
purchasing a bike

4. Prerequisite
• An SQL Server instance created (2005 or above)
• SQL Server Analysis Service (SSAS) –
Multidimensional Feature Installed
(this is used to host and browse the mining structures; cube is not required for data mining!)
• AdventureWorksDW database attached
(download from CodePlex - tailor to the SQL Server version you have)
• Visual Studio 2010 or above with SQL Server
Data Tools (SSDT) installed

5. My Demo Setup
• Visual Studio 2010
• SQL Server 2012

6. Create Data Mining Project
• Name the project as DM Decision Trees
(DM = Data Mining)

8. Create Data Source and Impersonation

10. Create Data Source View

12. Create Mining Structure
• Choose Microsoft Decision Trees model
• Select Data Source View
• Choose training data
• Select Input/Predict parameters
• Set content types
• Set Holdout percentage
• Name the mining structure and model

24. Deploy the mining structure and
model

26. Process the mining model
• This is also called “training the model”

28. Mining Model Viewer
• Identify dominant attributes
• Left is associative with more important
attributes
• Rich visualization is good for data exploration
as well

30. Mining Model Accuracy Chart
• This is called “Testing the Model” using the
Holdout data
• Lift chart
• Profit chart

34. Mining Model Prediction
• Singleton query
• Mass prediction

41. Browse mining model on SQL Server
• Decision trees
• Dependency network

45. Summary
• Microsoft Decision Trees is a powerful data
mining model, yet it is easy to build, train and use
• Can perform both Singleton (e.g. embed in an
app) and Mass Predictions (e.g. targeted
marketing)
• Holdout data can be used to test trained model
• Rich visualizations such as Lift/Profit Charts and
Dependency Network can facilitate analysis and
data exploration
• Relational database can be used for data mining;
cube is not required

46. The End