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Predicting Continuous Attributes in Decision Tree ,what does the background color of each node represent on the Decision Tree of the viewer ?

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    Predicting Continuous Attributes in Decision Tree ,what does the background color of each node represent on the Decision Tree of the viewer ?"The diamond chart has a line that represents the range of the attribute. The diamond is located at the mean for the node," the range of the attribute = max-min? the mean=AVERAGE ? how do I calculate the location of the diamond?

    http://msdn.microsoft.com/en-us/library/ms174503.aspx

     

    Decision Tree

    Predicting Discrete Attributes

    When a tree is built with a discrete predictable attribute, the viewer displays the following on each node in the tree:

    • The condition that caused the split.

    • A histogram that represents the distribution of the states of the predictable attribute, ordered by popularity.

    You can use the Histogram option to change the number of states that appear in the histograms in the tree. This is useful if the predictable attribute has many states. The states appear in a histogram in order of popularity from left to right; if the number of states that you choose to display is fewer than the total number of states in the attribute, the least popular states are displayed collectively in gray. To see the exact count for each state for a node, pause the pointer over the node to view an InfoTip, or select the node to view its details in the Mining Legend.

    The background color of each node represents the concentration of cases of the particular attribute state that you select by using the Background option. You can use this option to highlight nodes that contain a particular target in which you are interested.

    Predicting Continuous Attributes

    When a tree is built with a continuous predictable attribute, the viewer displays a diamond chart, instead of a histogram, for each node in the tree. The diamond chart has a line that represents the range of the attribute. The diamond is located at the mean for the node, and the width of the diamond represents the variance of the attribute at that node. A thinner diamond indicates that the node can create a more accurate prediction. The viewer also displays the regression equation, which is used to determine the split in the node.


    Wednesday, June 08, 2011 9:15 AM

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