Data mining by example - building predictive model using microsoft decision trees
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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.
Data mining by example - building predictive model using microsoft decision trees
- 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