在EM中如何评判决策树模型的优劣

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在EM13.1中运行一遍系统自带数据HMEQ（信贷风险模型），使用参数如下：
1）        Date Partition: Training -70; Validation -30; Test -0.
2）        Decision Tree: Interval Target Criterion-ProbF; Nominal Target Criterion -ProbChisq; Ordinal Target Criterion-Entropy; Maximum Depth-6; Leaf size-5.
结果如图。

我的问题：
1）        如何评判决策树模型的优劣？
2）        在哪里查看模型准确性和置信区间？

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关键词：决策树 树模型 Validation CRITERION partition

结果说明：Score Rankings Matrix — The Score Rankings Matrix plot overlays the selected statistics for standard, baseline and best models in a lattice that is defined by the training and validation data sets. The Score Ranking Matrix plots
Cumulative Lift
Lift
Gain
% Response
Cumulative % Response
% Captured Response
Cumulative % Captured Response.

结果说明2   Leaf Statistics — displays a bar chart of summary statistics for the leaves of the currently selected subtree. For binary targets, bars represent the observed probability of event for each terminal leaf in the tree. If there is validation data, then there will be a second bar for each node that represents the probability of event in the validation data.

结果说明3   Tree Map — displays a compact graphical display of the tree.

A default tree in the Tree Map plot has the following properties:
The nodes are colored according to the proportion of observations in the training data for that node that match the target value.
The node width is proportional to the number of observations in the node.
If you select a node in the Tree Map window, then the corresponding node is selected in the Tree window. If a leaf node is selected, then the corresponding bars in the Leaf Statistics plot are selected.

结果说明4

Fit Statistics Table

  
The Fit Statistics table displays the following statistics for the training, validation, and test data sets (if available):
_NOBS_ — Sum of Frequencies
_SUMW_ — Sum of Case Weights Times Freq
_MISC_ — Misclassification Rate
_MAX_ — Maximum Absolute Error
_SSE_ — Sum of Square Errors
_ASE_ — Average Sum of Squares
_RASE_ — Root Average Sum of Squares
_DIV_ — Divisor for ASE
_DFT_ — Total Degrees of Freedom
_APROF_ — Average Profit for Target
_PROF_ — Total Profit for Target
_PASE_ — Average Squared Error with Priors
_PMISC_ — Misclassification Rate with Priors