| Using Regression Tree in
XLMiner™:
In XLMinerTM, choose Prediction --> Regression Tree. The following dialog box appears, where you enter the data range that needs to be processed. Select the variables you want to use in your analysis.
Variables in input data: This box lists all the variables present in the dataset. If the "First row contains headers" box is checked, the header row above the data is used to identify variable names. Input Variables : Select one or more variables as independent variables from the Variables box by clicking on the corresponding selection button. These variables constitute the predictor variables. Output Variable: Select one variable as the dependent variable from the Variables box by clicking on the corresponding selection button. This is the variable being classified.
Click Next and the following dialog box appears:
In the following dialog box, choose the required outputs: Normalize input data: Normalizing the data (subtracting the mean and dividing by the standard deviation) is important to ensure that the distance measure accords equal weight to each variable -- without normalization, the variable with the largest scale will dominate the measure. Maximum #of splits : Enter the number of splits you can allow for input variables. Minimum #records in a terminal node: The tree building will proceed until all the terminal nodes reach this size. Scoring option : You can select which tree should be used for scoring. The option, Maximum #decision nodes in the pruned tree, becomes active when you select Using user specified tree. Click Next, and the following dialog box comes up, where you have the option to display a full tree, pruned tree or minimum error tree. Maximum #levels to be displayed : Enter here how many nodes you would like to be displayed in the output. Full tree: Check this box to display the full regression tree. The tree will be drawn according to the maximum #levels in the tree that are specified. Pruned tree: Check this box to display the pruned tree. Minimum error tree : Check this box to display minimum error tree, pruned using validation data. Score training data: Select this option to show an assessment of the performance of the tree in classifying the training data. The report is displayed according to your specifications - Detailed, Summary and Lift charts. Score validation data: Select this option to show an assessment of the performance of the tree in classifying the validation data. The report is displayed according to your specifications - Detailed, Summary and Lift charts. Score Test Data: The options in this group let you apply the model for scoring to the test partition (if one had been created earlier). The option "Score Test Data" is available only if the dataset contains test partition. Select it to apply the model to test data. Score new Data: The options in this group let you apply the model for scoring to an altogether new data. Specify where the new data is located. See the Example of Discriminant Analysis for detailed instructions on this. Score New data in database : See the Example of Discriminant Analysis for detailed instructions on this. See also: |
