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You created three models, yet you still don t know what additional information you got, or how you can use it. In this section, we ll start with examining mining models in BIDS, in the Data Mining Designer, with the help of built-in Data Mining Viewers. The viewers show you patterns and rules in an intuitive way. After the overview of the models, you have to decide which one you re going to deploy in production. We ll use the Lift Chart built-in tool to evaluate the models. Finally, we re going to simulate the
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deployment by creating a prediction query. We ll use the Data Mining Extensions (DMX) language with a special DMX prediction join to join the mining model with the ProspectiveBuyer table and predict which of the prospective customers is more likely to buy a bike.
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Viewing the models
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To make an overview of the mining models, follow these steps:
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In BIDS, in the Data Mining Designer window, click the Mining Model Viewer tab. If the TM_DT model isn t selected by default in the Mining Model dropdown list on the top left of the window, select it. Verify that you have the Decision Tree tab open. In the Background drop-down list, select value 1 of the Bike Buyer to check the potential buyers only. We re not interested in groups of customers that aren t going to buy a bike. Note the color of the nodes: the darker the color is, the more bike buyers appear in the node. For example, the node that groups people for whom the Number Cars Owned attribute is equal to 0 and Region is Pacific is quite dark in color. Therefore, the potential bike buyers are in that node. From the Mining Legend window, you can see more detailed information: more than 91 percent of people in this node have bought a bike in the past. You can see this information shown in figure 3.
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In the Dependency Network viewer, use the slider on the left side of the screen to show the strongest links only. Try to identify the two variables with the highest influence on the Bike Buyer attribute. Navigate to the Mining Model Viewer tab and select the TM_NB model to view the model that uses the Na ve Bayes algorithm. The first viewer is the Dependency Network viewer. Does the Na ve Bayes algorithm identify the same two variables as having the highest influence on the Bike Buyer attribute Different algorithms make slightly different predictions. Check other Na ve Bayes viewers as well. The Attribute Discrimination viewer is useful: it lets you see the values of input attributes that favor value 1 of the Bike Buyer attribute and the values that favor value 0. Check the Neural Network model. The only viewer you ll see is the Attribute Discrimination viewer, in which you can again find the values of input attributes that favor value 1 of the Bike Buyer attribute and those values that favor value 0. In addition, you can filter the viewer to show the discrimination for specific states of input attributes only.
Evaluating the models
As you probably noticed, different models find slightly different reasons for customers decisions whether to buy a bike. The question is how you can evaluate which model performs the best. The answer is quite simple in SQL Server 2008. Remember that when you created the models, you split the data into training and test sets. The model was trained on the training set only; you can make the predictions on the test set. Because you already know the outcome of the predictive variable in the test set, you can measure how accurate the predictions are. A standard way to show the accuracy is the Lift Chart. You can see a Lift Chart created using the data and models from this section in figure 4. In figure 4, you ll notice five curves and lines on the chart; yet, you created only three models. The three curves show the performance of the predictive models you created, and the two lines represent the Ideal Model and the Random Guess Model. The x axis shows the percentage of population (all cases), and the y axis shows the percentage of the target population (bike buyers in this example). The Ideal Model line (the topmost line) shows that approximately 50 percent of the customers of Adventure Works buy bikes. If you want to know exactly who s going to buy a bike and who isn t, you d need only 50 percent of the population to get all bike buyers. The lowest line is the Random Guess line. If you picked cases out of the population randomly, you d need 100 percent of the cases for 100 percent of bike buyers. Likewise, you d need 80 percent of the population for 80 percent of bike buyers, 60 percent of the population for 60 percent of bike buyers, and so on. The mining models you created give better results than the Random Guess Model, and of course worse results than the Ideal Model. In the Lift Chart, you can see the lift of the mining models from the Random Guess line; this is where the name Lift Chart comes from. Any model predicts the outcome with less than 100 percent of probability in all ranges of the population;
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