SAS Enterprise Miner

User : richc
Date : 17MAR2006:14:38:54
Notes:

"EM Workspace" :

EM Workspace

RCLIB.SATRAIN

Input Data Settings:


  • All variables

  • Interval Variables

  • Class Variables

  • Notes: not available

    RCLIB.SAVALID

    Input Data Settings:


  • All variables

  • Interval Variables

  • Class Variables

  • Notes: not available

    RCLIB.SATEST

    Input Data Settings:


  • All variables

  • Interval Variables

  • Class Variables

  • Notes: not available

    Tree

    Model assessment plot:

    SAS Graphics 

     
 Fit Statistic                     Training    Validation       Test 
  
 Average Squared Error                0.153        0.197       0.237 
 Sum of Squared Errors               70.661       45.419      55.070 
 Root Average Squared Error           0.391        0.444       0.487 
 Maximum Absolute Error               0.846        1.000       1.000 
 Divisor for ASE                    462.000      230.000     232.000 
 Total Degrees of Freedom           231.000         .           . 
 Misclassification Rate               0.195        0.261       0.336 
 Number of Estimated Weights          7.000         .           . 
 Sum of Frequencies                 231.000      115.000     116.000 
 Sum Case Weights * Frequencies     462.000      230.000     232.000 
  
     
                          N *             V N * 
 Node    Leaf      N    PRIORS    V N    PRIORS     % V 0     % V 1       % 0       % 1 
  
  26       1      37       37      28      28       64.29     35.71     81.08     18.92 
  38       2      16       16       4       4       25.00     75.00     25.00     75.00 
  39       3       5        5       2       2      100.00      0.00    100.00      0.00 
  15       4       5        5       2       2       50.00     50.00      0.00    100.00 
   9       5      13       13       4       4       25.00     75.00     15.38     84.62 
   5       6     110      110      61      61       80.33     19.67     82.73     17.27 
   3       7      45       45      14      14       35.71     64.29     28.89     71.11
  • English rules

  • Sequence

  • Matrix

    Target information
    Name: CHD
    Label: chd
    Measurement: binary

    Tree settings


    Splitting criterion: Gini Reduction
    Minimum number of observations in a leaf: 5
    Observations required for a split search: 10
    Maximum number of branches from a node: 2
    Maximum depth of tree: 6
    Splitting rules saved in each node: 5
    Surrogate rules saved in each node: 3
    Do not treat missing as an acceptable value
    Model assessment measure: Mis-Classification Rate
    Subtree: Best assessment value
    Observations sufficient for split search: 232
    Maximum tries in an exhaustive split search: 5000
    Do not use profit matrix during split search
    Do not use prior probability in split search

  • Log

  • Score Code
    Model assessment settings
    Train data set is not selected for assessment.
    Validation data set is selected for assessment.
    Test data set is not selected for assessment.
    Scored data set: 5000 observations are saved for interactive model assessment.

    SAS Graphics

    SAS Graphics

    SAS Graphics

    SAS Graphics

    Confusion Matrix (Assessed Partition=VALIDATION)

  • Notes: not available

    End Report

    Path Information


    Name: Tree_T1AQ1JRU
    Target: CHD
    Description:
    Mining Function: Transform
    Subject: No subject
    Rating: 0

  • Metadata Information

  • Input Variables Required for Scoring

  • Output Variables Produced by Scoring

  • Target Variables

  • Datastep Score Code

  • C Score Code