credit card promo-clustering
TRANSCRIPT
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Income Mag Promo Watch Promo Life Ins Promo Credit Card Ins. Sex
1 40-50,000 yes no no no Male
2 30-40,000 yes yes yes no Female
3 40-50,000 no no no no Male
4 30-40,000 yes yes yes yes Male
5 50-60,000 yes no yes no Female
6 20-30,000 no no no no Female
7 30-40,000 yes no yes yes Male
8 20-30,000 no yes no no Male
9 30-40,000 yes no no no Male
10 30-40,000 yes yes yes no Female
11 40-50,000 no yes yes no Female
12 20-30,000 no yes yes no Male
13 50-60,000 yes yes yes no Female
14 40-50,000 no yes no no Male
15 20-30,000 no no yes yes Female
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XLMiner : Categorical Score Variables
Income Mag Promo Watch Promo Life Ins Promo Credit Card Ins.
Row Id. Income Mag Promo Watch PromoLife Ins
Promo
Credit Card
Ins.Sex
1 40,000-50,000 yes no no no Male
2 30,000-40,000 yes yes yes no Female
3 40,000-50,000 no no no no Male
4 30,000-40,000 yes yes yes yes Male
5 50,000-60,000 yes no yes no Female
6 20,000-30,000 no no no no Female
7 30,000-40,000 yes no yes yes Male
8 20,000-30,000 no yes no no Male
9 30,000-40,000 yes no no no Male10 30,000-40,000 yes yes yes no Female
11 40,000-50,000 no yes yes no Female
12 20,000-30,000 no yes yes no Male
13 50,000-60,000 yes yes yes no Female
14 40,000-50,000 no yes no no Male
15 20,000-30,000 no no yes yes Female
Method of categorization Ordinal
Selected variables
Data
Data source Crdit Card data!$J$5:$P$19
#records in input data 15
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Sex Age
Age Income_ordMag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ordSex_ord Age_ord
45 2 1 0 0 0 1 10
40 1 1 1 1 0 0 6
42 2 0 0 0 0 1 8
43 1 1 1 1 1 1 9
38 3 1 0 1 0 0 4
55 0 0 0 0 0 0 11
35 1 1 0 1 1 1 3
27 0 0 1 0 0 1 1
43 1 1 0 0 0 1 9
41 1 1 1 1 0 0 7
43 2 0 1 1 0 0 9
29 0 0 1 1 0 1 2
39 3 1 1 1 0 0 5
55 2 0 1 0 0 1 11
19 0 0 0 1 1 0 0
Date : 14-Jun-2013 21:56:37
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XLMiner : Categorical Score Variables
Income Mag Promo Watch Promo Life Ins Promo Credit Card Ins.
Row Id. Income Mag Promo Watch PromoLife Ins
Promo
Credit Card
Ins.Sex
1 40,000-50,000 yes no no no Male
2 30,000-40,000 yes yes yes no Female
3 40,000-50,000 no no no no Male
4 30,000-40,000 yes yes yes yes Male
5 50,000-60,000 yes no yes no Female
6 20,000-30,000 no no no no Female
7 30,000-40,000 yes no yes yes Male
8 20,000-30,000 no yes no no Male
9 30,000-40,000 yes no no no Male10 30,000-40,000 yes yes yes no Female
11 40,000-50,000 no yes yes no Female
12 20,000-30,000 no yes yes no Male
13 50,000-60,000 yes yes yes no Female
14 40,000-50,000 no yes no no Male
15 20,000-30,000 no no yes yes Female
Method of categorization Ordinal
Selected variables
Data
Data source Crdit Card data!$J$5:$P$19
#records in input data 15
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Sex Age
Age Income_ordMag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ordSex_ord Age_ord
45 2 1 0 0 0 1 10
40 1 1 1 1 0 0 6
42 2 0 0 0 0 1 8
43 1 1 1 1 1 1 9
38 3 1 0 1 0 0 4
55 0 0 0 0 0 0 11
35 1 1 0 1 1 1 3
27 0 0 1 0 0 1 1
43 1 1 0 0 0 1 9
41 1 1 1 1 0 0 7
43 2 0 1 1 0 0 9
29 0 0 1 1 0 1 2
39 3 1 1 1 0 0 5
55 2 0 1 0 0 1 11
19 0 0 0 1 1 0 0
Date : 14-Jun-2013 10:47:52
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XLMiner : Categorical Score Variables
Mag Promo Watch Promo Life Ins Promo Credit Card Ins.Sex
Row Id. Income Mag Promo Watch PromoLife Ins
Promo
Credit Card
Ins.Sex
1 40-50,000 yes no no no Male
2 30-40,000 yes yes yes no Female
3 40-50,000 no no no no Male
4 30-40,000 yes yes yes yes Male
5 50-60,000 yes no yes no Female
6 20-30,000 no no no no Female
7 30-40,000 yes no yes yes Male
8 20-30,000 no yes no no Male
9 30-40,000 yes no no no Male10 30-40,000 yes yes yes no Female
11 40-50,000 no yes yes no Female
12 20-30,000 no yes yes no Male
13 50-60,000 yes yes yes no Female
14 40-50,000 no yes no no Male
15 20-30,000 no no yes yes Female
Method of categorization Ordinal
Selected variables
Data
Data source Sheet2!$J$5:$P$19
#records in input data 15
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Age
AgeMag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ordSex_ord Age_ord
45 2 1 1 1 2 11
40 2 2 2 1 1 7
42 1 1 1 1 2 9
43 2 2 2 2 2 10
38 2 1 2 1 1 5
55 1 1 1 1 1 12
35 2 1 2 2 2 4
27 1 2 1 1 2 2
43 2 1 1 1 2 10
41 2 2 2 1 1 8
43 1 2 2 1 1 10
29 1 2 2 1 2 3
39 2 2 2 1 1 6
55 1 2 1 1 2 12
19 1 1 2 2 1 1
Date : 27-May-2013 17:33:01
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XLMiner : Hierarchical Clustering
Inputs Dendrogram
Elapsed Time
Inputs
Income_ordMag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Clustering Stages
Stage Cluster 1 Cluster 2 Distance
1 2 10 12 1 9 1.414214
3 5 13 1.414214
4 8 12 1.414214
5 1 3 1.732051
6 1 4 1.732051
7 1 14 1.732051
8 1 11 2
9 2 5 2.236068
10 7 8 2.236068
11 7 15 2.236068
12 1 2 2.44949
13 1 6 2.44949
14 1 7 2.645751
Elapsed Time
15.00
Input data-
' ' '
Output Navigator
Clustering Stages
Predicted Clusters
Data
Draw dendrogram Yes
# Records in the input data 15
Input variables normalized No
Data Type Raw data
Variables
# Selected Variables 7
Selected variables
Parameters/Options
Selected clustering method Single linkage
Overall (secs)
Show cluster membership Yes
# Clusters 3
Selected Similarity measure Euclidean distance
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Credit Card
Ins._ordSex_ord Age_ord
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XLMiner : Hierarchical Clustering - Predicted clusters
Row Id. Cluster Id Sub Cluster Id Income_ordMag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
1 1 1 2 1 0 02 1 2 1 1 1 1
3 1 3 2 0 0 0
4 1 4 1 1 1 1
5 1 5 3 1 0 1
6 2 6 0 0 0 0
7 3 7 1 1 0 1
8 3 8 0 0 1 0
9 1 9 1 1 0 0
10 1 10 1 1 1 1
11 1 11 2 0 1 1
12 3 12 0 0 1 1
13 1 13 3 1 1 1
14 1 14 2 0 1 0
15 3 15 0 0 0 1
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Credit Card
Ins._ordSex_ord Age_ord
0 1 100 0 6
0 1 8
1 1 9
0 0 4
0 0 11
1 1 3
0 1 1
0 1 9
0 0 7
0 0 9
0 1 2
0 0 5
0 1 111 0 0
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XLMiner : Hierarchical Clustering - Dendrogram
Back to Navigator
1 9 3 4 14 11 2 10 5 13 60
0.5
1
1.5
2
2.5
3
0 1 2 3 4 5 6 7 8 9 10 11
Distance
Dendrogram(Single linkage)
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Date: 14-Jun-2013 22:00:38
7 8 12 15 0
15000
12 13 14 15 16
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(Ver: 12.5.2E)
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7 0
7 1
8 1
8 0
1 0
1 1.4142142 1.414214
2 0
9 0
9 1.414214
10 1.414214
10 0
13 0
13 1.414214
14 1.414214
14 0
1.5 1.414214
1.5 1.732051
3 1.732051
3 0
2.25 1.732051
2.25 1.732051
4 1.732051
4 0
3.125 1.732051
3.125 1.732051
5 1.7320515 0
4.0625 1.732051
4.0625 2
6 2
6 0
7.5 1
7.5 2.236068
9.5 2.236068
9.5 1.414214
12 012 2.236068
13.5 2.236068
13.5 1.414214
12.75 2.236068
12.75 2.236068
15 2.236068
15 0
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5.03125 2
5.03125 2.44949
8.5 2.44949
8.5 2.236068
6.765625 2.44949
6.765625 2.4494911 2.44949
11 0
8.882813 2.44949
8.882813 2.645751
13.875 2.645751
13.875 2.236068
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1 1
2 9
3 3
4 4
5 14
6 11
7 28 10
9 5
10 13
11 6
12 7
13 8
14 12
15 15
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XLMiner : k-Means Clustering
Inputs
Elapsed Time
Inputs
Income_ordMag
Promo_ord
Watch
Promo_ord
Cluster centers
Cluster Income_ordMag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ord
Cluster-1 0 0 0.666667 0.666667 0.333333
Cluster-2 1.8 1 0.6 1 0.2Cluster-3 1.42857 0.428571 0.428571 0.285714 0.142857
Distance between
cluster centersCluster-1 Cluster-2 Cluster-3
Cluster-1 0 4.53774296 8.71402669
Cluster-2 4.53774296 0 4.70848292
Cluster-3 8.71402669 4.70848292 0
Data summary
Cluster #Obs
Average
distance in
cluster
Cluster-1 3 1.216
Cluster-2 5 1.816
Cluster-3 7 1.584
Overall 15 1.588
Elapsed Time
34.00
Output Navigator
Cluster Centers Data Summ.
Predicted Clusters Random Starts Summary
# Clusters 3
Data
Input data-
' ' '# Records in the input data 15
Input variables normalized No
Variables
# Selected Variables 7
Selected variables
Parameters/Options
Show distance from each cluster Yes
Overall (secs)
Start Option Fixed Start
# Iterations 50
Show data summary Yes
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Life Ins
Promo_ord
Credit Card
Ins._ordSex_ord Age_ord
Sex_ord Age_ord
0.666667 1
0.2 5
0.714286 9.57143
Date: 14-Jun-2013 22:06:10
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XLMiner : k-Means Clustering - Predicted Clusters
Row Id. Cluster id Dist clust-1 Dist clust-2 Dist clust-3 Income_ordMag
Promo_ord
1 3 9.3333 5.2038 1.0973 2 12 2 5.2705 1.3711 3.8253 1 1
3 3 7.356 3.4756 1.8295 2 0
4 3 8.1718 4.2521 1.5779 1 1
5 2 4.4845 1.6971 5.9213 3 1
6 3 10.072 6.456 2.2497 0 0
7 2 2.6667 2.506 6.7234 1 1
8 1 0.88192 4.6989 8.7295 0 0
9 3 8.1921 4.322 1.0973 1 1
10 2 6.2272 2.2091 2.9137 1 1
11 3 8.2932 4.1569 1.4846 2 0
12 1 1.2019 3.7523 7.7775 0 0
13 2 5.1747 1.2961 5.0061 3 1
14 3 10.236 6.2354 1.7496 2 0
15 1 1.5635 5.5027 9.7865 0 0
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Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ordSex_ord Age_ord
0 0 0 1 101 1 0 0 6
0 0 0 1 8
1 1 1 1 9
0 1 0 0 4
0 0 0 0 11
0 1 1 1 3
1 0 0 1 1
0 0 0 1 9
1 1 0 0 7
1 1 0 0 9
1 1 0 1 2
1 1 0 0 5
1 0 0 1 110 1 1 0 0
Date: 14-Jun-2013 22:06:14
Back to Navigator
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XLMiner : Classification Tree
Inputs
Elapsed Time
Prior Class Pr
Train Log
Prune Log
Inputs
Income_ordMag
Promo_ord
Watch
Promo_ord
Credit Card
Ins._ord
Prior class probabilities
Prob.
0.5
0.5
Training Log (Growing the full tree using training data)
# Decision
Nodes% Error
0 40
1 20
2 20
Output Navigator
Train. Score - Summary Valid. Score - Summary Test Score - Summary Databas
Train. Score - Detailed Rep. Valid. Score - Detailed Rep. Test Score - Detailed Rep. New Score -
Training Lift Charts Validation Lift Charts Test Lift Charts
Input variables normalized No
Full Tree Rules Best Pruned Tree Rules Minimum Error Tree Rules User Specif ie
Full Tree Best Pruned Tree Minimum Error Tree User Spec
Data
Training data used for building the model-
' ' '# Records in the training data 15
Is pruning done No
Variables
# Input Variables 6
Input variables
Output variable Life Ins Promo_ord
Parameters/Options
Early stopping of tree growth required Yes
Minimum # records in a terminal node 1
0
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3 13.33
4 6.67
5 0
Full Tree Rules (Using Training Data)
5 6
Level NodeID ParentID SplitVar SplitValue Cases LeftChild
0 0 N/A Age_ord 7.5 15 1
1 1 0 Age_ord 1.5 8 3
1 2 0 tch Promo_ord 0.5 7 5
2 3 1 tch Promo_ord 0.5 2 7
2 4 1 N/A N/A 6 N/A
2 5 2 N/A N/A 4 N/A
2 6 2 Age_ord 10 3 9
3 7 3 N/A N/A 1 N/A
3 8 3 N/A N/A 1 N/A
3 9 6 N/A N/A 2 N/A
3 10 6 N/A N/A 1 N/A
Training Data scoring - Summary Report (Using Full Tree)
0.5
Actual Class 1 0
1 9 0
0 0 6
Class # Cases # Errors % Error
1 9 0 0.00
0 6 0 0.00
Overall 15 0 0.00
Elapsed Time
25.00
Error Report
Overall (secs)
#Decision Nodes #Terminal Nodes
Cut off Prob.Val. for Success (Updatable) ( Updating the value here will NOT update
Classification Confusion Matrix
Predicted Class
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(Ver: 12.5.2E)
Sex_ord Age_ord
Date: 14-Jun-2013 22:09:10
e Score
etailed Rep.
d Tree Rules
ified Tree
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RightChild Class Node Type
2 1 Decision
4 1 Decision
6 0 Decision
8 1 Decision
N/A 1 Terminal
N/A 0 Terminal
10 1 Decision
N/A 1 Terminal
N/A 0 Terminal
N/A 1 Terminal
N/A 0 Terminal
value in detailed report )
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0 0 0 0
1 1 1 3
0 0 0 0
1 1 1 3
1 1 1 3
0 0 0 0
1 1 1 3
0 0 0 0
0 0 0 0
1 1 1 3
1 1 1 3
1 1 1 3
1 1 1 3
0 0 0 01 1 1 3
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$D$10:$Q$25
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XLMiner : Classification Tree - Classification of Training Data (Using
0.5
Row Id.Predicted
ClassActual Class
Prob. for 1
(success)Income_ord
Mag
Promo_ord
Watch
Promo_ord
1 0 0 0 2 1 0
2 1 1 1 1 1 1
3 0 0 0 2 0 0
4 1 1 1 1 1 1
5 1 1 1 3 1 0
6 0 0 0 0 0 0
7 1 1 1 1 1 0
8 0 0 0 0 0 1
9 0 0 0 1 1 0
10 1 1 1 1 1 1
11 1 1 1 2 0 1
12 1 1 1 0 0 1
13 1 1 1 3 1 1
14 0 0 0 2 0 1
15 1 1 1 0 0 0
Data range-
' ' '
Cut off Prob.Val. for Success (Updatable) ( Updating the value here will NOT update
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ee) (Ver: 12.5.2E)
Std.Dev. Min. Max.
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
0 0 1
0 0 0
Date: 14-Jun-2013 22:09:20
Back to Navigator
4 5 6 7 8 9 10
Deciles
e lift chart (training dataset)
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/ Global mean
1.6666666671.666666667
1.666666667
1.666666667
1.666666667
1.666666667
1.666666667
1.666666667
1.666666667
0
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(Ver: 12.5.2E)Date: 14-Jun-2013 22:09:16
Rectangle denotes a TERMINAL NODE
0
ord
1
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ecessarily reflect what type of variable was originally input.
Sex_ord Age_ord
Continuous Continuous
Number Number
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(Ver: 12.5.2E)
Sex_ord Age_ord
Date: 14-Jun-2013 22:15:54
e Score
etailed Rep.
d Tree Rules
ified Tree
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RightChild Class Node Type
2 1 Decision4 1 Decision
6 0 Decision
N/A 0 Terminal
N/A 1 Terminal
N/A 0 Terminal
N/A 1 Terminal
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XLMiner : Classification Tree - Classification of Training Data (Using Full
Row Id.Predicted
ClassActual Class Prob. for class 0 Prob. for class 1 Income_ord
Mag
Promo_ord
1 0 0 1 0 2 1
2 1 1 0 1 1 1
3 0 0 1 0 2 0
4 1 1 0.333332986 0.666666985 1 1
5 1 1 0 1 3 1
6 0 0 1 0 0 0
7 1 1 0 1 1 1
8 0 0 0.5 0.5 0 0
9 0 0 1 0 1 1
10 1 1 0 1 1 1
11 1 1 0.333332986 0.666666985 2 0
12 1 1 0 1 0 0
13 1 1 0 1 3 1
14 1 0 0.333332986 0.666666985 2 0
15 0 1 0.5 0.5 0 0
Mismatches between Predicted and Actual class is highlighted in green
Data range-
' ' '
Class with the highest probability is highlighted in yellow
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ree) (Ver: 12.5.2E)
Watch
Promo_ord
Credit Card
Ins._ordSex_ord Age_ord
0 0 1 10
1 0 0 6
0 0 1 8
1 1 1 9
0 0 0 4
0 0 0 11
0 1 1 3
1 0 1 1
0 0 1 9
1 0 0 7
1 0 0 9
1 0 1 2
1 0 0 5
1 0 1 11
0 1 0 0
Date: 14-Jun-2013 22:16:01
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XLMiner : Classification Tree
Income Mag Promo Watch Promo Life Ins Promo
Categorical Categorical Categorical Categorical
String String String String
Income_ord
Promo_ord
Promo_ord
Ins._ord
Input Input Input Input
Prob.
0.5
0.5
Level NodeID ParentID SplitVar SplitValue Cases LeftChild
0 0 N/A Age_ord 7.5 15 1
1 1 0 Age_ord 1.5 8 3
1 2 0 tch Promo_ord 0.5 7 5
2 3 1 N/A N/A 2 N/A
2 4 1 N/A N/A 6 N/A
2 5 2 N/A N/A 4 N/A
2 6 2 N/A N/A 3 N/A
DataSource
WorkBook Path C:\Users\neelesh\Desktop
WorkBook Name Credit Card promo-CLUSTERING.xlsx
Training Range [CategoryVar2]!$D$11:$Q$25
#Training Rows 15
#Variables in Data set 14
Pruning No
#Selected Variables 7
Data Dictionary
Variables in Data Set
Variable Type*
Variable Data Type
Mining Schema
Selected Variables
Variable Type
Inputs Normalised No
Full Tree Rules
Classes in Input Data set
# Classes 2
Class 1 0
Class 2 1
Prior class probabilities
Class
0
1
Model
Other Options
Minimum # records in a terminal node 2Initial cutoff probability value 0.5
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ecessarily reflect what type of variable was originally input.
Sex_ord Age_ord
Continuous Continuous
Number Number
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XLMiner : Classification Tree
Inputs
Elapsed Time
Prior Class Pr
Train Log
Prune Log
Inputs
Mag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ord
Prior class probabilities
Prob.
0.25
0.25
0.250.25
Training Log (Growing the full tree using training data)
# Decision
Nodes% Error
0 66.67
1 40
Output Navigator
Train. Score - Summary Valid. Score - Summary Test Score - Summary Databas
Train. Score - Detailed Rep. Valid. Score - Detailed Rep. Test Score - Detailed Rep. New Score -
Training Lift Charts Validation Lift Charts Test Lift Charts
Input variables normalized No
Full Tree Rules Best Pruned Tree Rules Minimum Error Tree Rules User Specif ie
Full Tree Best Pruned Tree Minimum Error Tree User Spec
Data
Training data used for building the model-
' ' '# Records in the training data 15
Is pruning done No
Variables
# Input Variables 6
Input variables
Output variable Income_ord
Parameters/Options
Early stopping of tree growth required Yes
Minimum # records in a terminal node 1
2
Max # levels displayed in tree drawing 5
Draw full tree Yes
Output options chosen
Summary report of scoring on training data
Detailed report of scoring on training data
Equal prior probabilities
Class
0
1
3
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2 2
1 1
2 2
1 1
3 3
0 0
1 1
0 0
1 1
1 1
2 2
0 0
3 3
2 20 0
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$D$10:$Q$25
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) (Ver: 12.5.2E)
Mag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ordSex_ord Age_ord
1 0 0 0 1 10
1 1 1 0 0 6
0 0 0 0 1 8
1 1 1 1 1 9
1 0 1 0 0 4
0 0 0 0 0 11
1 0 1 1 1 3
0 1 0 0 1 1
1 0 0 0 1 9
1 1 1 0 0 7
0 1 1 0 0 9
0 1 1 0 1 2
1 1 1 0 0 5
0 1 0 0 1 11
0 0 1 1 0 0
Date: 14-Jun-2013 22:18:02
Back to Navigator
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XLMiner : Classification Tree - Full Tree (Using Training Data)
Back to Navigator Circle denotes a DECISION NODE
Place the cursor on a Decision Node to read the decision rule
0.5
5
00.5 0.5
0.52 3 1
0 2
Mag Promo_ord
Age_ord
Watch Promo_ord Credit Card Ins._ord
Sex_ord
7 8
3 4 3
2 2 2 1
1 1
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(Ver: 12.5.2E)Date: 14-Jun-2013 22:18:00
Rectangle denotes a TERMINAL NODE
5.5
9.5
1 2
Age_ord
Age_ord
5
4 1
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XLMiner : Classification Tree
Income Mag Promo Watch Promo Life Ins Promo
Categorical Categorical Categorical Categorical
String String String String
Promo_ord
Promo_ord
Promo_ord
Ins._ord
Input Input Input Input
Prob.
0.25
0.250.25
0.25
Level NodeID ParentID SplitVar SplitValue Cases LeftChild
0 0 N/A ag Promo_ord 0.5 15 1
1 1 0 Age_ord 5 7 3
1 2 0 Age_ord 5.5 8 5
2 3 1 N/A N/A 3 N/A
2 4 1 tch Promo_ord 0.5 4 7
2 5 2 it Card Ins._ord 0.5 3 92 6 2 Age_ord 9.5 5 11
3 7 4 Sex_ord 0.5 2 13
3 8 4 N/A N/A 2 N/A
3 9 5 N/A N/A 2 N/A
3 10 5 N/A N/A 1 N/A
3 11 6 N/A N/A 4 N/A
3 12 6 N/A N/A 1 N/A
4 13 7 N/A N/A 1 N/A
4 14 7 N/A N/A 1 N/A
DataSource
WorkBook Path C:\Users\neelesh\Desktop
WorkBook Name Credit Card promo-CLUSTERING.xlsx
Training Range [CategoryVar2]!$D$11:$Q$25
#Training Rows 15
#Variables in Data set 14
Pruning No
#Selected Variables 7
Data Dictionary
Variables in Data Set
Variable Type*
Variable Data Type
Mining Schema
Selected Variables
Variable Type
Inputs Normalised No
Class
Classes in Input Data set
# Classes 4
Class 1 0
Class 2 1
Class 3 2
Class 4 3
Prior class probabilities
0
12
3
Model
Full Tree Rules
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Other Options
Minimum # records in a terminal node 1
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(Ver: 12.5.2E)
*This is an indication of how XLMiner stores this variable for later retrieval; it does not n
redit Card Ins. Sex Age Income_ord ag Promo_ord tch Promo_ord Ins Promo_ord it Card Ins._ord
Categorical Categorical Continuous Continuous Continuous Continuous Continuous Continuous
String String Number Number Number Number Number Number
Sex_ord Age_ord Income_ord
Input Input Output
RightChild Class Node Type Operator Prob. for 0 Prob. for 1 Prob. for 2 Prob. for 3
2 1 Decision
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ecessarily reflect what type of variable was originally input.
Sex_ord Age_ord
Continuous Continuous
Number Number
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XLMiner : Classification Tree
Inputs
Elapsed Time
Prior Class Pr
Train Log
Prune Log
Inputs
Income_ordMag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Prior class probabilities
Prob.
0.083333333
0.083333333
0.0833333330.083333333
0.083333333
0.083333333
0.083333333
0.083333333
0.083333333
0.083333333
0.083333333
0.083333333
Output Navigator
Train. Score - Summary Valid. Score - Summary Test Score - Summary Databas
Train. Score - Detailed Rep. Valid. Score - Detailed Rep. Test Score - Detailed Rep. New Score -
Training Lift Charts Validation Lift Charts Test Lift Charts
Input variables normalized No
Full Tree Rules Best Pruned Tree Rules Minimum Error Tree Rules User Specif ie
Full Tree Best Pruned Tree Minimum Error Tree User Spec
Data
Training data used for building the model-
' ' '# Records in the training data 15
Variables
# Input Variables 6
Input variables
Output variable Age_ord
Summary report of scoring on training data
Parameters/Options
Early stopping of tree growth required Yes
Minimum # records in a terminal node 1
Is pruning done No
Max # levels displayed in tree drawing 7
Draw full tree Yes
Output options chosen
8
Detailed report of scoring on training data
Equal prior probabilities
Class
0
1
23
4
5
6
7
9
10
11
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Training Log (Growing the full tree using training data)
# Decision
Nodes% Error
0 80
1 73.33
2 66.67
3 60
4 60
5 53.33
6 46.67
7 40
8 33.33
9 26.67
10 20
11 13.33
12 13.33
13 6.67
Full Tree Rules (Using Training Data)
13 14
Level NodeID ParentID SplitVar SplitValue Cases LeftChild
0 0 N/A ag Promo_ord 0.5 15 1
1 1 0 Ins Promo_ord 0.5 7 3
1 2 0 Sex_ord 0.5 8 5
2 3 1 Income_ord 1 4 7
2 4 1 Income_ord 1 3 9
2 5 2 Income_ord 2 4 11
2 6 2 Income_ord 1.5 4 13
3 7 3 tch Promo_ord 0.5 2 153 8 3 tch Promo_ord 0.5 2 17
3 9 4 tch Promo_ord 0.5 2 19
3 10 4 N/A N/A 1 N/A
3 11 5 N/A N/A 2 N/A
3 12 5 tch Promo_ord 0.5 2 21
3 13 6 tch Promo_ord 0.5 3 23
3 14 6 N/A N/A 1 N/A
4 15 7 N/A N/A 1 N/A
4 16 7 N/A N/A 1 N/A
4 17 8 N/A N/A 1 N/A
4 18 8 N/A N/A 1 N/A
4 19 9 N/A N/A 1 N/A
4 20 9 N/A N/A 1 N/A4 21 12 N/A N/A 1 N/A
4 22 12 N/A N/A 1 N/A
4 23 13 Ins Promo_ord 0.5 2 25
4 24 13 N/A N/A 1 N/A
5 25 23 N/A N/A 1 N/A
5 26 23 N/A N/A 1 N/A
Training Data scoring - Summary Report (Using Full Tree)
#Decision Nodes #Terminal Nodes
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Actual Class 0 1 2 3 4 5
0 1 0 0 0 0 0
1 0 1 0 0 0 0
2 0 0 1 0 0 0
3 0 0 0 1 0 0
4 0 0 0 0 1 0
5 0 0 0 0 0 1
6 0 0 0 0 0 0
7 0 0 0 0 0 0
8 0 0 0 0 0 0
9 0 0 0 0 0 0
10 0 0 0 0 0 0
11 0 0 0 0 0 0
Class # Cases # Errors % Error
0 1 0 0.00
1 1 0 0.00
2 1 0 0.00
3 1 0 0.00
4 1 0 0.00
5 1 0 0.00
6 1 0 0.00
7 1 1 100.00
8 1 0 0.00
9 3 0 0.00
10 1 0 0.00
11 2 0 0.00
Overall 15 1 6.67
Elapsed Time
25.00
Predicted Class
Error Report
Overall (secs)
Classification Confusion Matrix
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(Ver: 12.5.2E)
Credit Card
Ins._ordSex_ord
Date: 14-Jun-2013 22:20:16
e Score
etailed Rep.
d Tree Rules
ified Tree
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10 10
6 6
8 8
9 9
4 4
11 11
3 3
1 1
9 9
6 7
9 9
2 2
5 5
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$D$10:$Q$25
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XLMiner : Classification Tree - Classification of Training Data (Using Full Tre
Row Id.Predicted
ClassActual Class Prob. for class 0 Prob. for class 1 Prob. for class 2 Prob. for class 3
1 10 10 0 0 0 0
2 6 6 0 0 0 0
3 8 8 0 0 0 0
4 9 9 0 0 0 0
5 4 4 0 0 0 0
6 11 11 0 0 0 0
7 3 3 0 0 0 1
8 1 1 0 1 0 0
9 9 9 0 0 0 0
10 6 7 0 0 0 0
11 9 9 0 0 0 0
12 2 2 0 0 1 0
13 5 5 0 0 0 0
14 11 11 0 0 0 0
15 0 0 1 0 0 0
Mismatches between Predicted and Actual class is highlighted in green
Data range-
' ' '
Class with the highest probability is highlighted in yellow
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) (Ver: 12.5.2E)
Prob. for class 4 Prob. for class 5 Prob. for class 6 Prob. for class 7 Prob. for class 8 Prob. for class 9Prob. for class
10
0 0 0 0 0 0 1
0 0 0.5 0.5 0 0 0
0 0 0 0 1 0 0
0 0 0 0 0 1 0
1 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 1 0
0 0 0.5 0.5 0 0 0
0 0 0 0 0 1 0
0 0 0 0 0 0 0
0 1 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
Date: 14-Jun-2013 22:20:24
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Prob. for class
11Income_ord
Mag
Promo_ord
Watch
Promo_ord
Life Ins
Promo_ord
Credit Card
Ins._ordSex_ord
0 2 1 0 0 0 1
0 1 1 1 1 0 0
0 2 0 0 0 0 1
0 1 1 1 1 1 1
0 3 1 0 1 0 0
1 0 0 0 0 0 0
0 1 1 0 1 1 1
0 0 0 1 0 0 1
0 1 1 0 0 0 1
0 1 1 1 1 0 0
0 2 0 1 1 0 00 0 0 1 1 0 1
0 3 1 1 1 0 0
1 2 0 1 0 0 1
0 0 0 0 1 1 0
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XLMiner : Classification Tree - Full Tree (Using Training Data)
Back to Navigator Circle denotes a DECISION NODE
Place the cursor on a Decision Node to read the decision rule
0.5
1
0.5 0.5
11 1 8 11
Life Ins Promo_ord
Income_ord
Watch Promo_ord Watch Promo_ord
4
2 2
1 1 1 1
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(Ver: 12.5.2E)Date: 14-Jun-2013 22:20:22
Rectangle denotes a TERMINAL NODE
0.5
1 2
0.59 6
0.5
0 2 4
Mag Promo_ord
Income_ord Income_ord
Watch Promo_ord Watch Promo_o
7 8
3 4
2 1 2 2
1 1 1 1
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0.5
1.5
0.510
50.5
9
9 3
Sex_ord
Income_ord
rd Watch Promo_ord
Life Ins Promo_ord
4
3 1
2 1
1 1
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XLMiner : Classification Tree
Income Mag Promo Watch Promo Life Ins Promo
Categorical Categorical Categorical Categorical
String String String String
Income_ord
Promo_ord
Promo_ord
Promo_ord
Input Input Input Input
Prob.
0.083333333
0.083333333
0.083333333
0.083333333
0.083333333
0.083333333
0.0833333330.083333333
0.083333333
0.083333333
0.083333333
0.083333333
DataSource
WorkBook Path C:\Users\neelesh\Desktop
WorkBook Name Credit Card promo-CLUSTERING.xlsx
Variable Data Type
Training Range [CategoryVar2]!$D$11:$Q$25
#Training Rows 15
#Variables in Data set 14
#Selected Variables 7
Data Dictionary
Variables in Data Set
Variable Type*
Class 2 1
Mining Schema
Selected Variables
Variable Type
Inputs Normalised No
Pruning No
Classes in Input Data set
# Classes 12
Class 1 0
Class 3 2
Class 4 3
Class 5 4
Class 6 5
Class 7 6
Class 8 7
Class 9 8
Class 10 9Class 11 10
7
Class 12 11
Prior class probabilities
Class
0
1
2
3
4
5
6
8
9
10
11
Model
Full Tree Rules
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Level NodeID ParentID SplitVar SplitValue Cases LeftChild
0 0 N/A ag Promo_ord 0.5 15 1
1 1 0 Ins Promo_ord 0.5 7 3
1 2 0 Sex_ord 0.5 8 5
2 3 1 Income_ord 1 4 7
2 4 1 Income_ord 1 3 9
2 5 2 Income_ord 2 4 112 6 2 Income_ord 1.5 4 13
3 7 3 tch Promo_ord 0.5 2 15
3 8 3 tch Promo_ord 0.5 2 17
3 9 4 tch Promo_ord 0.5 2 19
3 10 4 N/A N/A 1 N/A
3 11 5 N/A N/A 2 N/A
3 12 5 tch Promo_ord 0.5 2 21
3 13 6 tch Promo_ord 0.5 3 23
3 14 6 N/A N/A 1 N/A
4 15 7 N/A N/A 1 N/A
4 16 7 N/A N/A 1 N/A
4 17 8 N/A N/A 1 N/A
4 18 8 N/A N/A 1 N/A
4 19 9 N/A N/A 1 N/A
4 20 9 N/A N/A 1 N/A
4 21 12 N/A N/A 1 N/A
4 22 12 N/A N/A 1 N/A
4 23 13 Ins Promo_ord 0.5 2 25
4 24 13 N/A N/A 1 N/A
5 25 23 N/A N/A 1 N/A
5 26 23 N/A N/A 1 N/A
Other Options
Minimum # records in a terminal node 1
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(Ver: 12.5.2E)
*This is an indication of how XLMiner stores this variable for later retrieval; it does not n
redit Card Ins. Sex Age Income_ord ag Promo_ord tch Promo_ord Ins Promo_ord it Card Ins._ord
Categorical Categorical Continuous Continuous Continuous Continuous Continuous Continuous
String String Number Number Number Number Number Number
Ins._ord Sex_ord Age_ord
Input Input Output
Date: 14-Jun-2013 22:20:26
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RightChild Class Node Type Operator Prob. for 0 Prob. for 1 Prob. for 2 Prob. for 3
2 9 Decision
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ecessarily reflect what type of variable was originally input.
Sex_ord Age_ord
Continuous Continuous
Number Number
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Prob. for 4 Prob. for 5 Prob. for 6 Prob. for 7 Prob. for 8 Prob. for 9 Prob. for 10 Prob. for 11
0.066667 0.066667 0.066667 0.066667 0.066667 0.2 0.066667 0.133333
0 0 0 0 0.142857 0.142857 0 0.285714
0.125 0.125 0.125 0.125 0 0.25 0.125 0
0 0 0 0 0.25 0 0 0.5
0 0 0 0 0 0.333333 0 0
0.25 0.25 0.25 0.25 0 0 0 00 0 0 0 0 0.5 0.25 0
0 0 0 0 0 0 0 0.5
0 0 0 0 0.5 0 0 0.5
0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0
0 0 0.5 0.5 0 0 0 0
0.5 0.5 0 0 0 0 0 0
0 0 0 0 0 0.666667 0 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
0 0 0 0 0 0.5 0 0
0 0 0 0 0 1 0 0
0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0
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XLMiner : Classification Tree
Inputs
Elapsed Time
Prior Class Pr
Train Log
Prune Log
Inputs
Income_ordCredit Card
Ins._ordSex_ord Age_ord
Prior class probabilities
Prob.
0.5
0.5
Training Log (Growing the full tree using training data)
# Decision
Nodes% Error
0 40
1 20
2 20
3 20
Output Navigator
Train. Score - Summary Valid. Score - Summary Test Score - Summary Databas
Train. Score - Detailed Rep. Valid. Score - Detailed Rep. Test Score - Detailed Rep. New Score -
Training Lift Charts Validation Lift Charts Test Lift Charts
Input variables normalized No
Full Tree Rules Best Pruned Tree Rules Minimum Error Tree Rules User Specif ie
Full Tree Best Pruned Tree Minimum Error Tree User Spec
Data
Training data used for building the model-
' ' '# Records in the training data 15
Variables
# Input Variables 4
Input variables
Output variable Life Ins Promo_ord
Summary report of scoring on training data
Parameters/Options
Early stopping of tree growth required Yes
Minimum # records in a terminal node 1
Is pruning done No
Max # levels displayed in tree drawing 5
Draw full tree Yes
Output options chosen
Detailed report of scoring on training data
Equal prior probabilities
Class
0
1
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4 13.33
5 6.67
6 6.67
7 0
Full Tree Rules (Using Training Data)
7 8
Level NodeID ParentID SplitVar SplitValue Cases LeftChild
0 0 N/A Age_ord 7.5 15 1
1 1 0 Age_ord 1.5 8 3
1 2 0 Age_ord 9.5 7 5
2 3 1 it Card Ins._ord 0.5 2 7
2 4 1 N/A N/A 6 N/A
2 5 2 Age_ord 8.5 4 9
2 6 2 N/A N/A 3 N/A
3 7 3 N/A N/A 1 N/A
3 8 3 N/A N/A 1 N/A
3 9 5 N/A N/A 1 N/A
3 10 5 Income_ord 1.5 3 11
4 11 10 it Card Ins._ord 0.5 2 13
4 12 10 N/A N/A 1 N/A
5 13 11 N/A N/A 1 N/A
5 14 11 N/A N/A 1 N/A
Training Data scoring - Summary Report (Using Full Tree)
Actual Class 0 1
0 6 0
1 0 9
Class # Cases # Errors % Error
0 6 0 0.00
1 9 0 0.00
Overall 15 0 0.00
Elapsed Time
24.00
#Terminal Nodes
Classification Confusion Matrix
Predicted Class
Error Report
Overall (secs)
#Decision Nodes
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(Ver: 12.5.2E)Date: 14-Jun-2013 22:26:46
e Score
etailed Rep.
d Tree Rules
ified Tree
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RightChild Class Node Type
2 1 Decision
4 1 Decision
6 0 Decision
8 0 Decision
N/A 1 Terminal
10 0 Decision
N/A 0 Terminal
N/A 0 Terminal
N/A 1 Terminal
N/A 0 Terminal12 1 Decision
14 0 Decision
N/A 1 Terminal
N/A 0 Terminal
N/A 1 Terminal
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0 0
1 1
0 0
1 1
1 1
0 0
1 1
0 0
0 0
1 1
1 1
1 1
1 1
0 01 1
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ree) (Ver: 12.5.2E)
Sex_ord Age_ord
1 10
0 6
1 8
1 9
0 4
0 11
1 3
1 1
1 9
0 7
0 9
1 2
0 5
1 11
0 0
Date: 14-Jun-2013 22:26:53
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XLMiner : Classification Tree - Full Tree (Using Training Data)
Back to Navigator Circle denotes a DECISION NODE
Place the cursor on a Decision Node to read the decision rule
7.5
1.5 9.5
0.51
8.50
0 1 01.5
Sub Tree
beneath1
Age_ord
Age_ord Age_ord
Credit Card Ins._ord Age_ord
Income_ord
8 7
2 6 4 3
1 1 1 3
2 1
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(Ver: 12.5.2E)Date: 14-Jun-2013 22:26:52
Rectangle denotes a TERMINAL NODE
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XLMiner : Classification Tree
Income Mag Promo Watch Promo Life Ins Promo
Categorical Categorical Categorical Categorical
String String String String
Income_ord
Ins._ord Sex_ord Age_ord
Input Input Input Input
Prob.
0.5
0.5
Level NodeID ParentID SplitVar SplitValue Cases LeftChild
0 0 N/A Age_ord 7.5 15 1
1 1 0 Age_ord 1.5 8 3
1 2 0 Age_ord 9.5 7 5
2 3 1 it Card Ins._ord 0.5 2 7
2 4 1 N/A N/A 6 N/A
2 5 2 Age_ord 8.5 4 9
2 6 2 N/A N/A 3 N/A
3 7 3 N/A N/A 1 N/A
3 8 3 N/A N/A 1 N/A
3 9 5 N/A N/A 1 N/A3 10 5 Income_ord 1.5 3 11
4 11 10 it Card Ins._ord 0.5 2 13
4 12 10 N/A N/A 1 N/A
5 13 11 N/A N/A 1 N/A
5 14 11 N/A N/A 1 N/A
DataSource
WorkBook Path C:\Users\neelesh\Desktop
WorkBook Name Credit Card promo-CLUSTERING.xlsx
Training Range [CategoryVar2]!$D$11:$Q$25
#Training Rows 15
#Variables in Data set 14
Pruning No
#Selected Variables 5
Data Dictionary
Variables in Data Set
Variable Type*
Variable Data Type
Mining Schema
Selected Variables
Variable Type
Inputs Normalised No
Full Tree Rules
Classes in Input Data set
# Classes 2
Class 1 0
Class 2 1
Prior class probabilities
Class
0
1
Model
Other Options
Minimum # records in a terminal node 1
Initial cutoff probability value 0.5
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(Ver: 12.5.2E)
*This is an indication of how XLMiner stores this variable for later retrieval; it does not n
redit Card Ins. Sex Age Income_ord ag Promo_ord tch Promo_ord Ins Promo_ord it Card Ins._ord
Categorical Categorical Continuous Continuous Continuous Continuous Binary Continuous
String String Number Number Number Number Number Number
Promo_ord
Output
RightChild Class Node Type Operator Prob. for 0 Prob. for 1
2 1 D i i < 0 4 0 6
Date: 14-Jun-2013 22:26:55