Delivering Business Intelligence with Microsoft SQL Server 2005

534 Delivering Business Intelligence with Microsoft SQL Server 2005

in the training data set. The other columns show the actual value for the number of

children at home for each customer.

In Figure 14-13, the top grid shows the result for the Decision Trees mining

model. Looking at the top row in the grid, we can see that in 805 cases, the Decision

Trees mining model predicted three children at home when there were actually

three children at home. These were correct predictions. In 109 cases, the Decision

Trees mining model predicted three children at home when there were actually four

children at home. These predictions were in error.

The diagonal of the grid shows the correct predictions: predicted three with

actual three, predicted two with actual two, predicted four with actual four, and so

on. We want to have the largest numbers along the diagonal. This is the case for the

Decision Trees mining model. We already know this model was accurate. The Naïve

Bayes mining model, shown in the middle grid in Figure 14-13, does not have the

largest numbers along the diagonal. This mining model had a tendency to predict

two children at home when there were actually four children at home. This mistake

occurred 994 times during the processing of our testing data set.

Mining Model Prediction

All this training and testing has fi nally gotten us to a place where we have a mining

model ready to make real-world predictions. We have seen how each of the mining

models does at making predictions; now, we can pick one and put it to work. The

Mining Model Prediction tab enables us to do just that.

Using the Mining Model Prediction tab, we can create queries that use a mining

model to make predictions. Two types of prediction queries are supported: a

singleton query and a prediction join. We look at the singleton query fi rst.

A Singleton Query

A singleton query lets us feed a single set of input values to the mining model. We

receive a single value for the predictable based on these values. This enables us to

manually enter a scenario to see what the mining model will predict.

Creating a Singleton Query

For both the singleton query and the prediction join, we must fi rst select the mining

model to use. When the model is selected, the Singleton Query Input dialog box

contains an input fi eld for each input column in the mining model. We can then enter

values for each of these input columns.

We then select the columns we would like in the result set. The predictable

Chapter 14: Spelunking—Exploration Using Data Mining 535

column should be included in the result set; otherwise, the query isn’t doing much

for us. We can also include our own custom expressions, as desired.

When the query is designed, we switch to the result view to see the result set. We

can also switch to the SQL view to see the DMX query being created for us behind

the scenes. More on that in the section “Data Mining Extensions.” Queries can be

saved to be rerun at a later time.

Learn By Doing—Creating a Singleton Query

Feature Highlighted



Creating a singleton query using the Mining Model Prediction tab

Business Need Our business need is simple. We will try out the mining model we

have worked so hard to create.

Steps

1. Open the Business Intelligence Development Studio.

2. Open the MaxMinSalesDM project.

3. If the Data Mining Design tab for the Classification - Children At Home data

mining structure is not displayed, double-click the entry for this data mining

structure in the Solution Explorer window.

4. Select the Mining Model Prediction tab on the Data Mining Design tab.

5. Click the Singleton Query button in the Mining Model Prediction tab toolbar.

This is shown in Figure 14-14.

Figure 14-14 The Singleton Query toolbar button