Data
ipums_la_97-small

ipums_la_97-small

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Author: IPUMS (ipums@hist.umn.edu) Donor: Stephen Bay (sbay@ics.uci.edu) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/IPUMS+Census+Database) - 1999 Please cite: IPUMS Database This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the years 1970, 1980, and 1990. The coding schemes have been standardized (by the IPUMS project) to be consistent across years. The original source for this data set is the IPUMS project (RugglesSobek, 1997). The IPUMS project is a large collection of federal census data which has standardized coding schemes to make comparisons across time easy. The data is an unweighted 1 in 100 sample of responses from the Los Angeles -- Long Beach area for the years 1970, 1980, and 1990. The household and individual records were flattened into a single table and we used all variables that were available for all three years. When there was more than one version of a variable, such as for race, we used the most general. For occupation and industry we used the 1950 basis. Note that PUMS data is based on cluster samples, i.e. samples are made of households or dwellings from which there may be multiple individuals. Individuals from the same household are no longer independent. Ruggles (1995) considers this issue further and discusses its effect (along with the effects of stratification) on standard errors. The variable schltype appears to have different coding values across the years 1970, 1980, and 1990. There are two versions of this data set. The small data set contains a 1 in 1000 sample of the Los Angeles and Long Beach area. It was formed by sampling from the large data set. The large data set contains a 1 in 100 sample of the Los Angeles and Long Beach area. Past Usage S. D. Bay and M. J. Pazzani. (1999) "Detecting Group Differences: Mining Contrast Sets". submitted. Copyright Information All persons are granted a limited license to use and distribute this documentation and the accompanying data, subject to the following conditions: * No fee may be charged for use or distribution. * Publications and research reports based on the database must cite it appropriately. The citation should include the following: Steven Ruggles and Matthew Sobek et. al. Integrated Public Use Microdata Series: Version 2.0 Minneapolis: Historical Census Projects, University of Minnesota, 1997 If possible, citations should also include the URL for the IPUMS site: http://www.ipums.umn.edu/. In addition, we request that users send us a copy of any publications, research reports, or educational material making use of the data or documentation. Send all electronic material to ipums@hist.umn.edu References 1. http://www.ipums.umn.edu/ 2. mailto:ipums@hist.umn.edu 3. http://www.ics.uci.edu/~sbay 4. mailto:sbay@ics.uci.edu 5. http://www.ipums.umn.edu/ 6. mailto:ipums@hist.umn.edu 7. http://www.ipums.umn.edu/ 8. http://www.census.gov/ 9. http://kdd.ics.uci.edu/ 10. http://www.ics.uci.edu/ 11. http://www.uci.edu/

61 features

movedin (target)nominal8 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
valuenominal12 unique values
0 missing
rentnominal154 unique values
0 missing
ftotincnominal409 unique values
0 missing
nfamsnominal5 unique values
0 missing
ncouplesnominal4 unique values
0 missing
nmothersnominal5 unique values
0 missing
nfathersnominal3 unique values
0 missing
momlocnominal12 unique values
0 missing
stepmomnominal4 unique values
0 missing
momrulenominal6 unique values
0 missing
poplocnominal8 unique values
0 missing
steppopnominal3 unique values
0 missing
poprulenominal5 unique values
0 missing
splocnominal8 unique values
0 missing
sprulenominal5 unique values
0 missing
famsizenominal15 unique values
0 missing
nchildnominal10 unique values
0 missing
nchlt5nominal6 unique values
0 missing
famunitnominal5 unique values
0 missing
eldchnominal66 unique values
0 missing
yngchnominal65 unique values
0 missing
nsibsnominal10 unique values
0 missing
relategnominal13 unique values
0 missing
agenominal97 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
occscorenominal45 unique values
0 missing
seinominal80 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
inctotnominal288 unique values
0 missing
incwagenominal216 unique values
0 missing
incbusnominal107 unique values
0 missing
incfarmnominal18 unique values
0 missing
incssnominal40 unique values
0 missing
incwelfrnominal38 unique values
0 missing
incothernominal101 unique values
0 missing
povertynominal488 unique values
0 missing
migrat5gnominal7 unique values
576 missing
migplac5nominal98 unique values
6276 missing
vetstatnominal2 unique values
4542 missing
tranworknominal9 unique values
4275 missing

107 properties

7019
Number of instances (rows) of the dataset.
61
Number of attributes (columns) of the dataset.
8
Number of distinct values of the target attribute (if it is nominal).
48089
Number of missing values in the dataset.
7019
Number of instances with at least one value missing.
0
Number of numeric attributes.
61
Number of nominal attributes.
18.65
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
8
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.29
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
0.11
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
49.03
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.79
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
94.87
Standard deviation of the number of distinct values among attributes of the nominal type.
0.55
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.55
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
1.3
Second quartile (Median) of entropy among attributes.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.29
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
0.66
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
27.61
Percentage of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.76
Entropy of the target attribute values.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1938
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
7.92
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.55
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
1
The minimal number of distinct values among attributes of the nominal type.
13.11
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.59
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.
0.81
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
100
Percentage of instances having missing values.
2.43
Third quartile of entropy among attributes.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
28.23
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
488
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
11.23
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.17
Average class difference between consecutive instances.
0.11
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
Maximum skewness among attributes of the numeric type.
3.68
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.79
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.55
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
258
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.13
Third quartile of mutual information between the nominal attributes and the target attribute.
0.55
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
0.72
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.92
Average entropy of the attributes.
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.92
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.29
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
0.11
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
Mean kurtosis among attributes of the numeric type.
0.68
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.79
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.55
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.1
Average mutual information between the nominal attributes and the target attribute.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.55
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
0.72
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

5 tasks

269 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: movedin
165 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: movedin
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: movedin
Define a new task