Data
ipums_la_99-small

ipums_la_99-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.

57 features

binaryClass (target)nominal2 unique values
0 missing
yearnominal1 unique values
0 missing
gqnominal3 unique values
0 missing
gqtypegnominal3 unique values
0 missing
farmnominal2 unique values
0 missing
ownershgnominal2 unique values
168 missing
valuenumeric26 unique values
0 missing
rentnumeric28 unique values
0 missing
ftotincnumeric3890 unique values
0 missing
nfamsnominal9 unique values
0 missing
ncouplesnominal5 unique values
0 missing
nmothersnominal5 unique values
0 missing
nfathersnominal5 unique values
0 missing
momlocnominal11 unique values
0 missing
stepmomnominal5 unique values
0 missing
momrulenominal7 unique values
0 missing
poplocnominal12 unique values
0 missing
steppopnominal4 unique values
0 missing
poprulenominal5 unique values
0 missing
splocnominal13 unique values
0 missing
sprulenominal6 unique values
0 missing
famsizenominal17 unique values
0 missing
nchildnominal9 unique values
0 missing
nchlt5nominal6 unique values
0 missing
famunitnominal9 unique values
0 missing
eldchnumeric67 unique values
0 missing
yngchnumeric67 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
5421 missing
schoolnominal2 unique values
428 missing
educrecnominal9 unique values
428 missing
schltypenominal3 unique values
428 missing
empstatgnominal3 unique values
2110 missing
labforcenominal2 unique values
2110 missing
occscorenumeric48 unique values
0 missing
seinumeric80 unique values
0 missing
classwkgnominal2 unique values
3671 missing
wkswork2nominal6 unique values
4198 missing
hrswork2nominal8 unique values
4782 missing
yrlastwknominal7 unique values
6172 missing
workedyrnominal2 unique values
2110 missing
inctotnumeric2241 unique values
0 missing
incwagenumeric1096 unique values
0 missing
incbusnumeric192 unique values
0 missing
incfarmnominal10 unique values
0 missing
incssnumeric545 unique values
0 missing
incwelfrnumeric217 unique values
0 missing
incothernumeric158 unique values
0 missing
povertynumeric502 unique values
0 missing
migrat5gnominal2 unique values
707 missing
movedinnominal7 unique values
0 missing
vetstatnominal2 unique values
2110 missing

107 properties

8844
Number of instances (rows) of the dataset.
57
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
34843
Number of missing values in the dataset.
8844
Number of instances with at least one value missing.
15
Number of numeric attributes.
42
Number of nominal attributes.
0.88
Average class difference between consecutive instances.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.12
Maximum skewness among attributes of the numeric type.
15.53
Minimum standard deviation of attributes of the numeric type.
6.91
Percentage of missing values.
-0.25
Third quartile of kurtosis among attributes of the numeric type.
0.71
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
425644.64
Maximum standard deviation of attributes of the numeric type.
6.42
Percentage of instances belonging to the least frequent class.
26.32
Percentage of numeric attributes.
63377.54
Third quartile of means among attributes of the numeric type.
0.06
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
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.13
Average entropy of the attributes.
568
Number of instances belonging to the least frequent class.
73.68
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
0.02
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
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.61
Mean kurtosis among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.84
First quartile of entropy among attributes.
1.32
Third quartile of skewness among attributes of the numeric type.
136955.62
Third quartile of standard deviation of attributes of the numeric type.
0.71
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
84686.38
Mean of means among attributes of the numeric type.
0.54
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.01
First quartile of kurtosis among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.06
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
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Average mutual information between the nominal attributes and the target attribute.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
73.03
First quartile of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.02
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
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
103.06
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
10
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.71
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
3.93
Standard deviation of the number of distinct values among attributes of the nominal type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
6.12
Average number of distinct values among the attributes of the nominal type.
-0.1
First quartile of skewness among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.06
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.11
Mean skewness among attributes of the numeric type.
38.68
First quartile of standard deviation of attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.02
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
0.09
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
93.58
Percentage of instances belonging to the most frequent class.
101572.03
Mean standard deviation of attributes of the numeric type.
1.02
Second quartile (Median) of entropy among attributes.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
Entropy of the target attribute values.
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
8276
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
-0.5
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.99
Maximum entropy among attributes.
-1.84
Minimum kurtosis among attributes of the numeric type.
15284.65
Second quartile (Median) of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
38.69
Maximum kurtosis among attributes of the numeric type.
15.95
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
632582.02
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.73
Second quartile (Median) of skewness among 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.
0.07
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
17.54
Percentage of binary attributes.
25377.84
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
31.78
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
17
The maximum number of distinct values among attributes of the nominal type.
-0.96
Minimum skewness among attributes of the numeric type.
100
Percentage of instances having missing values.
1.43
Third quartile of entropy among attributes.

8 tasks

465 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
218 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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