Exploring the Decision Tree Model (Basic Data Mining Tutorial)
Applies To: SQL Server 2016 Preview
The Microsoft Decision Trees algorithm predicts which columns influence the decision to purchase a bike based upon the remaining columns in the training set.
The Microsoft Decision Tree Viewer provides the following tabs for use in exploring decision tree mining models:
On the Decision Tree tab, you can view decision trees for every predictable attribute in the dataset.
In this case, the model predicts only one column, Bike Buyer, so there is only one tree to view. If there were more trees, you could use the Tree box to choose another tree.
As you view the TM_Decision_Tree model in the Decision Tree viewer, you can see the most important attributes at the left side of the chart. “Most important” means that these attributes have the greatest influence on the outcome. Attributes further down the tree (to the right of the chart) have less of an effect.
In this example, age is the single most important factor in predicting bike buying. The model groups customers by age, and then shows the next more important attribute for each age group. For example, in the group of customers aged 34 to 40, the number of cars owned is the strongest predictor after age.
Select the Mining Model Viewer tab in Data Mining Designer.
By default, the designer opens to the first model that was added to the structure -- in this case, TM_Decision_Tree.
Use the magnifying glass buttons to adjust the size of the tree display.
By default, the Microsoft Tree Viewer shows only the first three levels of the tree. If the tree contains fewer than three levels, the viewer shows only the existing levels. You can view more levels by using the Show Level slider or the Default Expansion list.
Slide Show Level to the fourth bar.
Change the Background value to 1.
By changing the Background setting, you can quickly see the number of cases in each node that have the target value of 1 for [Bike Buyer]. Remember that in this particular scenario, each case represents a customer. The value 1 indicates that the customer previously purchased a bike; the value 0 indicates that the customer has not purchased a bike. The darker the shading of the node, the higher the percentage of cases in the node that have the target value.
Place your cursor over the node labeled All. An tooltip will display the following information:
Total number of cases
Number of non bike buyer cases
Number of bike buyer cases
Number of cases with missing values for [Bike Buyer]
Alternately, place your cursor over any node in the tree to see the condition that is required to reach that node from the node that comes before it. You can also view this same information in the Mining Legend.
Click on the node for Age >=34 and < 41. The histogram is displayed as a thin horizontal bar across the node and represents the distribution of customers in this age range who previously did (pink) and did not (blue) purchase a bike. The Viewer shows us that customers between the ages of 34 and 40 with one or no cars are likely to purchase a bike. Taking it one step further, we find that the likelihood to purchase a bike increases if the customer is actually age 38 to 40.
Because you enabled drillthrough when you created the structure and model, you can retrieve detailed information from the model cases and mining structure, including those columns that were not included in the mining model (e.g., emailAddress, FirstName).
For more information, see Drillthrough Queries (Data Mining).
Right-click a node, and select Drill Through then Model Columns Only.
The details for each training case are displayed in spreadsheet format. These details come from the vTargetMail view that you selected as the case table when building the mining structure.
Right-click a node, and select Drill Through then Model and Structure Columns.
The same spreadsheet displays with the structure columns appended to the end.
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The Dependency Network tab displays the relationships between the attributes that contribute to the predictive ability of the mining model. The Dependency Network viewer reinforces our findings that Age and Region are important factors in predicting bike buying.
Click the Bike Buyer node to identify its dependencies.
The center node for the dependency network, Bike Buyer, represents the predictable attribute in the mining model. The graph highlights any connected nodes that have an effect on the predictable attribute.
Adjust the All Links slider to identify the most influential attribute.
As you drag down the slider, attributes that have only a weak effect on the [Bike Buyer] column are removed from the graph. By adjusting the slider, you can discover that Age and Region are the greatest factors in predicting whether someone is a bike buyer.
See these topics to explore the data using the other kinds of models.