Data
ipums_la_98-small

ipums_la_98-small

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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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.

56 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
132 missing
valuenumeric25 unique values
0 missing
rentnumeric26 unique values
0 missing
ftotincnumeric3594 unique values
0 missing
nfamsnominal8 unique values
0 missing
ncouplesnominal4 unique values
0 missing
nmothersnominal4 unique values
0 missing
nfathersnominal4 unique values
0 missing
momlocnominal11 unique values
0 missing
stepmomnominal5 unique values
0 missing
momrulenominal7 unique values
0 missing
poplocnominal10 unique values
0 missing
steppopnominal3 unique values
0 missing
poprulenominal5 unique values
0 missing
splocnominal10 unique values
0 missing
sprulenominal6 unique values
0 missing
famsizenominal15 unique values
0 missing
nchildnominal10 unique values
0 missing
nchlt5nominal4 unique values
0 missing
famunitnominal6 unique values
0 missing
eldchnumeric69 unique values
0 missing
yngchnumeric68 unique values
0 missing
nsibsnominal10 unique values
0 missing
relategnominal13 unique values
0 missing
agenumeric91 unique values
0 missing
sexnominal2 unique values
0 missing
racegnominal7 unique values
0 missing
marstnominal6 unique values
0 missing
chbornnominal13 unique values
4424 missing
schoolnominal2 unique values
0 missing
educrecnominal9 unique values
322 missing
schltypenominal3 unique values
5314 missing
empstatgnominal3 unique values
1811 missing
labforcenominal2 unique values
1811 missing
occscorenumeric47 unique values
0 missing
seinumeric82 unique values
0 missing
classwkgnominal2 unique values
3184 missing
wkswork2nominal6 unique values
3690 missing
hrswork2nominal8 unique values
4154 missing
workedyrnominal2 unique values
1811 missing
inctotnumeric2048 unique values
0 missing
incwagenumeric1049 unique values
0 missing
incbusnumeric165 unique values
0 missing
incfarmnumeric19 unique values
0 missing
incssnumeric362 unique values
0 missing
incwelfrnumeric207 unique values
0 missing
incothernumeric283 unique values
0 missing
povertynumeric501 unique values
0 missing
migrat5gnominal5 unique values
3954 missing
movedinnominal7 unique values
0 missing
vetstatnominal2 unique values
1820 missing

107 properties

7485
Number of instances (rows) of the dataset.
56
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
32427
Number of missing values in the dataset.
7369
Number of instances with at least one value missing.
16
Number of numeric attributes.
40
Number of nominal attributes.
89.43
Percentage of instances belonging to the most frequent class.
149956.06
Mean standard deviation of attributes of the numeric type.
1.02
Second quartile (Median) of entropy among attributes.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
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
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
6694
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
-0.55
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.49
Entropy of the target attribute values.
2.92
Maximum entropy among attributes.
-1.96
Minimum kurtosis among attributes of the numeric type.
22710.09
Second quartile (Median) of means among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
49.91
Maximum kurtosis among attributes of the numeric type.
15.28
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
575337.81
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1.1
Second quartile (Median) of skewness among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.12
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
16.07
Percentage of binary attributes.
41625.78
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.6
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.
15
The maximum number of distinct values among attributes of the nominal type.
-0.98
Minimum skewness among attributes of the numeric type.
98.45
Percentage of instances having missing values.
1.55
Third quartile of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
25.89
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
7.14
Maximum skewness among attributes of the numeric type.
15.12
Minimum standard deviation of attributes of the numeric type.
7.74
Percentage of missing values.
-0.27
Third quartile of kurtosis among attributes of the numeric type.
0.81
Average class difference between consecutive instances.
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
449248.7
Maximum standard deviation of attributes of the numeric type.
10.57
Percentage of instances belonging to the least frequent class.
28.57
Percentage of numeric attributes.
239628.39
Third quartile of means among attributes of the numeric type.
0.73
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.11
Average entropy of the attributes.
791
Number of instances belonging to the least frequent class.
71.43
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
0.11
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
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.51
Mean kurtosis among attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.61
First quartile of entropy among attributes.
1.31
Third quartile of skewness among attributes of the numeric type.
0.03
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
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
103093.3
Mean of means among attributes of the numeric type.
0.54
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.93
First quartile of kurtosis among attributes of the numeric type.
422174.97
Third quartile of standard deviation of attributes of the numeric type.
0.73
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Average mutual information between the nominal attributes and the target attribute.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
73.57
First quartile of means among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.11
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
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
57.9
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
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
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
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.8
Average number of distinct values among the attributes of the nominal type.
0.07
First quartile of skewness among attributes of the numeric type.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.73
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
3.63
Standard deviation of the number of distinct values among attributes of the nominal type.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.04
Mean skewness among attributes of the numeric type.
39.19
First quartile of standard deviation of attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

8 tasks

456 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
206 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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