Data
kdd_ipums_la_97-small

kdd_ipums_la_97-small

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized binarized_regression_problem mythbusting_1 study_1 study_15 study_20 study_41
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

61 features

binaryClass (target)nominal2 unique values
0 missing
yearnominal1 unique values
0 missing
gqnominal3 unique values
0 missing
gqtypegnominal8 unique values
0 missing
farmnominal2 unique values
0 missing
ownershgnominal2 unique values
135 missing
valuenumeric12 unique values
0 missing
rentnumeric154 unique values
0 missing
ftotincnumeric409 unique values
0 missing
nfamsnumeric5 unique values
0 missing
ncouplesnumeric4 unique values
0 missing
nmothersnumeric5 unique values
0 missing
nfathersnumeric3 unique values
0 missing
momlocnumeric12 unique values
0 missing
stepmomnumeric4 unique values
0 missing
momrulenumeric6 unique values
0 missing
poplocnumeric8 unique values
0 missing
steppopnumeric3 unique values
0 missing
poprulenumeric5 unique values
0 missing
splocnumeric8 unique values
0 missing
sprulenumeric5 unique values
0 missing
famsizenumeric15 unique values
0 missing
nchildnumeric10 unique values
0 missing
nchlt5numeric6 unique values
0 missing
famunitnumeric5 unique values
0 missing
eldchnumeric66 unique values
0 missing
yngchnumeric65 unique values
0 missing
nsibsnumeric10 unique values
0 missing
relategnominal13 unique values
0 missing
agenumeric97 unique values
0 missing
sexnominal2 unique values
0 missing
racegnominal7 unique values
0 missing
marstnominal6 unique values
0 missing
chbornnominal13 unique values
4283 missing
bplgnominal103 unique values
0 missing
schoolnominal2 unique values
344 missing
educrecnominal9 unique values
344 missing
schltypenominal4 unique values
344 missing
empstatgnominal3 unique values
1772 missing
labforcenominal2 unique values
1772 missing
occ1950nominal191 unique values
3040 missing
occscorenumeric45 unique values
0 missing
seinumeric80 unique values
0 missing
ind1950nominal133 unique values
3040 missing
classwkgnominal2 unique values
3022 missing
wkswork2nominal6 unique values
3625 missing
hrswork2nominal8 unique values
4309 missing
yrlastwknominal7 unique values
4618 missing
workedyrnominal2 unique values
1772 missing
inctotnumeric288 unique values
0 missing
incwagenumeric216 unique values
0 missing
incbusnumeric107 unique values
0 missing
incfarmnumeric18 unique values
0 missing
incssnumeric40 unique values
0 missing
incwelfrnumeric38 unique values
0 missing
incothernumeric101 unique values
0 missing
povertynumeric488 unique values
0 missing
migrat5gnominal7 unique values
576 missing
migplac5nominal98 unique values
6276 missing
movedinnominal8 unique values
0 missing
vetstatnominal2 unique values
4542 missing

107 properties

7019
Number of instances (rows) of the dataset.
61
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
43814
Number of missing values in the dataset.
7019
Number of instances with at least one value missing.
33
Number of numeric attributes.
28
Number of nominal attributes.
63.04
Percentage of instances belonging to the most frequent class.
75319.64
Mean standard deviation of attributes of the numeric type.
1.08
Second quartile (Median) of entropy among attributes.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
4425
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
0.31
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
Entropy of the target attribute values.
4.36
Maximum entropy among attributes.
-1.99
Minimum kurtosis among attributes of the numeric type.
3.76
Second quartile (Median) of means among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1786.92
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0.08
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.02
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
486783.79
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1.14
Second quartile (Median) of skewness among attributes of the numeric type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.69
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
14.75
Percentage of binary attributes.
2.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
191
The maximum number of distinct values among attributes of the nominal type.
-0.89
Minimum skewness among attributes of the numeric type.
100
Percentage of instances having missing values.
2.08
Third quartile of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
5.18
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
41.56
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
10.23
Percentage of missing values.
11.37
Third quartile of kurtosis among attributes of the numeric type.
0.54
Average class difference between consecutive instances.
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
485340.19
Maximum standard deviation of attributes of the numeric type.
36.96
Percentage of instances belonging to the least frequent class.
54.1
Percentage of numeric attributes.
25324.53
Third quartile of means among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.56
Average entropy of the attributes.
2594
Number of instances belonging to the least frequent class.
45.9
Percentage of nominal attributes.
0.3
Third quartile of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
65.69
Mean kurtosis among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.88
First quartile of entropy among attributes.
2.2
Third quartile of skewness among attributes of the numeric type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
48835.09
Mean of means among attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.7
First quartile of kurtosis among attributes of the numeric type.
43338.18
Third quartile of standard deviation of attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.18
Average mutual information between the nominal attributes and the target attribute.
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.63
First quartile of means among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.04
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
7.52
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
9
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.8
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
23.07
Average number of distinct values among the attributes of the nominal type.
0.51
First quartile of skewness among attributes of the numeric type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
47.29
Standard deviation of the number of distinct values among attributes of the nominal type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.05
Mean skewness among attributes of the numeric type.
0.51
First quartile of standard deviation of attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

8 tasks

476 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
197 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: binaryClass
71 runs - estimation_procedure: 10-fold Learning Curve - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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