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

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

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

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Data Mining By Example – Building Predictive Model Using Microsoft

Decision Trees

by Shaoli Lu

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Microsoft Decision Trees

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

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Goal

• To predict a prospect’s likelihood of purchasing a bike

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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

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My Demo Setup

• Visual Studio 2010

• SQL Server 2012

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Create Data Mining Project

• Name the project as DM Decision Trees (DM = Data Mining)

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Create Data Source and Impersonation

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Create Data Source View

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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

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Deploy the mining structure and model

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Process the mining model

• This is also called “training the model”

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Mining Model Viewer

• Identify dominant attributes

• Left is associative with more important attributes

• Rich visualization is good for data exploration as well

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Mining Model Accuracy Chart

• This is called “Testing the Model” using the Holdout data

• Lift chart

• Profit chart

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Mining Model Prediction

• Singleton query

• Mass prediction

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Browse mining model on SQL Server

• Decision trees

• Dependency network

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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

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The End


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