SAS Enterprise Miner

User : Dan
Date : 28JUL2006:18:43:52
Notes:


"EM Workspace" :

EM Workspace


SASUSER.SMALLD

Input Data Settings:


  • All variables

  • Interval Variables

  • Class Variables

  • Notes: not available


    Data Partition

  • Partition Settings

  • Output

  • Log

  • Training Code

  • Notes


    Transform Variables

  • Interval Variables and Transformations

  • Notes


    NN-D3

    Optimization plot:

    Optimization

     
     Fit Statistic                     Training    Validation       Test 
      
     [ TARGET=RESP_FLG ]                    .            .           . 
     Average Profit                        0.28         0.28        0.30 
     Misclassification Rate                0.28         0.28        0.30 
     Average Error                         0.57         0.57        0.58 
     Average Squared Error                 0.19         0.19        0.20 
     Sum of Squared Errors              1375.19      1223.42      472.68 
     Root Average Squared Error            0.44         0.44        0.44 
     Root Mean Squared Error                .           0.44        0.44 
     Error Function                     4076.87      3623.09     1389.99 
     Mean Squared Error                     .           0.19        0.20 
     Maximum Absolute Error                0.88         0.87        0.87 
     Divisor for ASE                    7200.00      6400.00     2400.00 
     Model Degrees of Freedom           6391.00          .           . 
     Degrees of Freedom for Error      -2791.00          .           . 
     Total Degrees of Freedom           3600.00          .           . 
     Sum of Frequencies                 3600.00      3200.00     1200.00 
     Sum Case Weights * Frequencies     7200.00      6400.00     2400.00 
      
  • Network settings

  • Variables

  • Output

  • Log

  • Training Code

  • 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


    NN-D2

    Optimization plot:

    Optimization

     
     Fit Statistic                     Training    Validation       Test 
      
     [ TARGET=RESP_FLG ]                    .            .           . 
     Average Profit                        0.28         0.28        0.30 
     Misclassification Rate                0.28         0.28        0.30 
     Average Error                         0.59         0.59        0.61 
     Average Squared Error                 0.20         0.20        0.21 
     Sum of Squared Errors              1441.78      1290.22      501.68 
     Root Average Squared Error            0.45         0.45        0.46 
     Root Final Prediction Error           1.83          .           . 
     Root Mean Squared Error               1.33         0.45        0.46 
     Error Function                     4248.34      3794.85     1461.46 
     Mean Squared Error                    1.77         0.20        0.21 
     Maximum Absolute Error                0.72         0.72        0.72 
     Final Prediction Error                3.34          .           . 
     Divisor for ASE                    7200.00      6400.00     2400.00 
     Model Degrees of Freedom           3193.00          .           . 
     Degrees of Freedom for Error        407.00          .           . 
     Total Degrees of Freedom           3600.00          .           . 
     Sum of Frequencies                 3600.00      3200.00     1200.00 
     Sum Case Weights * Frequencies     7200.00      6400.00     2400.00 
     Akaike's Information Criterion    10634.34          .           . 
     Schwarz's Baysian Criterion       30394.82          .           . 
      
  • Network settings

  • Variables

  • Output

  • Log

  • Training Code

  • 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


    Assessment-NN

    SAS Graphics

    SAS Graphics

    SAS Graphics

    SAS Graphics


    Variable Selection



  • Results

  • Settings:


  • Variables

  • Output

  • Log

  • Training Code

  • Score Code

  • Notes


    Logit-D2

  • Parameters:

  • Fit Statistics
     
     Fit Statistic                         Training      Validation            Test 
      
     Akaike's Information Criterion    4000.3154453               .               . 
     Average Squared Error             0.1831276886    0.1926826553    0.1996994379 
     Average Error Function            0.5464327007    0.5690393158    0.5855879534 
     Degrees of Freedom for Error              3567               .               . 
     Model Degrees of Freedom                    33               .               . 
     Total Degrees of Freedom                  3600               .               . 
     Divisor for ASE                           7200            6400            2400 
     Error Function                    3934.3154453    3641.8516211    1405.4110881 
     Final Prediction Error            0.1865160899               .               . 
     Maximum Absolute Error            0.9690954513    0.9541993297    0.9340076785 
     Mean Square Error                 0.1848218892    0.1926826553    0.1996994379 
     Sum of Frequencies                        3600            3200            1200 
     Number of Estimate Weights                  33               .               . 
     Root Average Sum of Squares       0.4279342106    0.4389563251    0.4468774306 
     Root Final Prediction Error        0.431875086               .               . 
     Root Mean Squared Error           0.4299091639    0.4389563251    0.4468774306 
     Schwarz's Bayesian Criterion      4204.5421864               .               . 
     Sum of Squared Errors             1318.5193577     1233.168994    479.27865108 
     Sum of Case Weights Times Freq            7200            6400            2400 
     Misclassification Rate            0.2672222222       0.2821875             0.3 
     Total Profit for RESP_FLG                  997             896             356 
     Average Profit for RESP_FLG       0.2769444444            0.28    0.2966666667 
      

  • Target Information:

  • Regression Settings:

  • Output

  • Log

  • Training Code

  • 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


    Tree-D2

    Model assessment plot:

    SAS Graphics

     
     Fit Statistic                     Training    Validation       Test 
      
     Average Squared Error                 0.18         0.19        0.21 
     Sum of Squared Errors              1316.15      1213.74      495.03 
     Root Average Squared Error            0.43         0.44        0.45 
     Maximum Absolute Error                0.94         0.94        0.94 
     Divisor for ASE                    7200.00      6400.00     2400.00 
     Total Degrees of Freedom           3600.00          .           . 
     Misclassification Rate                0.27         0.28        0.29 
     Number of Estimated Weights           9.00          .           . 
     Sum of Frequencies                 3600.00      3200.00     1200.00 
     Sum Case Weights * Frequencies     7200.00      6400.00     2400.00 
      
     
                              N *             V N * 
     Node    Leaf     N     PRIORS    V N    PRIORS     % V 0     % V 1       % 0       % 1 
      
       4       1     157      157     149      149      45.64     54.36     43.95     56.05 
      10       2     431      431     351      351      70.94     29.06     73.78     26.22 
      11       3     699      699     614      614      61.56     38.44     58.66     41.34 
       6       4     308      308     326      326      64.11     35.89     59.42     40.58 
      50       5      47       47      51       51      60.78     39.22     61.70     38.30 
      82       6     974      974     911      911      82.22     17.78     84.80     15.20 
      83       7     125      125      93       93      70.97     29.03     71.20     28.80 
      27       8     340      340     276      276      92.03      7.97     93.82      6.18 
      15       9     519      519     429      429      69.93     30.07     69.36     30.64 
      
  • English rules

  • Sequence

  • Matrix

    Target information
    Name: RESP_FLG
    Label: Responder Flag(1:responder; 0:nonrespond
    Measurement: binary

    Tree settings


    Splitting criterion: Gini Reduction
    Minimum number of observations in a leaf: 15
    Observations required for a split search: 31
    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: 0
    Treat missing as an acceptable value
    Model assessment measure: Average Square Error (Gini index)
    Subtree: Best assessment value
    Observations sufficient for split search: 3600
    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


    Ensemble [Combined]

  • Settings

  • Variables

  • Output

  • Log

  • Training Code

  • 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)


    Assessment

    SAS Graphics

    SAS Graphics

    SAS Graphics

    SAS Graphics

    End Report


    Path Information


    Name: Assessment_T1WBG03T
    Target: RESP_FLG
    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