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humandevel

humandevel

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Author: Source: Unknown - Date unknown Please cite: Human Development Index [DATA] United Nations Development Program compiled an Index of Human Development. Column 1: Country(character) 2: Index 3: GNP GNP PER CAPITA RANK RANK - RANK HDI 1987 GNP RANK 1 Niger 0.116 20 -19 2 Mali 0.143 15 -13 3 Burkina Faso 0.150 13 -10 4 Sierra Leone 0.150 27 -23 5 Chad 0.157 4 1 6 Guinea 0.162 31 -25 7 Somalia 0.200 23 -16 8 Mauritania 0.208 40 -32 9 Afghanistan 0.212 17 -8 10 Benin 0.212 28 -18 11 Burundi 0.235 18 -7 12 Bhutan 0.236 3 9 13 Mozambique 0.239 10 3 14 Malawi 0.250 7 7 15 Sudan 0.255 32 -17 16 Central Afr. Rep. 0.258 29 -13 17 Nepal 0.273 8 9 18 Senegal 0.274 43 -25 19 Ethiopia 0.282 1 18 20 Zaire 0.294 5 15 21 Rwanda 0.304 26 -5 22 Angola 0.304 58 -36 23 Bangladesh 0.318 6 17 24 Nigeria 0.322 36 -12 25 Yemen Arab Rep. 0.328 47 -22 26 Liberia 0.333 42 -16 27 Togo 0.337 24 3 28 Uganda 0.354 21 7 29 Haiti 0.356 34 -5 30 Ghana 0.360 37 -7 31 Yemen PDR 0.369 39 -8 32 Cote d'Ivoire 0.393 52 -20 33 Congo 0.395 59 -26 34 Namibia 0.404 60 -26 35 Tanzania 0.413 12 23 36 Pakistan 0.423 33 3 37 India 0.439 25 12 38 Madagascar 0.440 14 24 39 Papua New Guinea 0.471 50 -11 40 Kampuchea 0.471 2 38 41 Cameroon 0.474 64 -23 42 Kenya 0.481 30 12 43 Zambia 0.481 19 24 44 Morocco 0.489 48 -4 45 Egypt 0.501 49 -4 46 Laos 0.506 9 37 47 Gabon 0.525 93 -46 48 Oman 0.535 104 -56 49 Bolivia 0.548 44 5 50 Burma (Myanmar) 0.561 11 39 51 Honduras 0.563 53 -2 52 Zimbabwe 0.576 45 7 53 Lesotho 0.580 35 18 54 Indonesia 0.591 41 13 55 Guatemala 0.592 63 -8 56 Viet Nam 0.608 16 40 57 Algeria 0.609 91 -34 58 Botwswana 0.646 69 -11 59 El Salvador 0.651 56 3 60 Tunisia 0.657 70 -10 61 Iran 0.660 97 -36 62 Syria 0.691 79 -17 63 Dominican Rep. 0.699 51 12 64 Saudi Arabia 0.702 107 -43 65 Philipines 0.714 46 19 66 China 0.716 22 44 67 Libya 0.719 103 -36 68 South Africa 0.731 82 -14 69 Lebanon 0.735 78 -9 70 Mongolia 0.737 57 13 71 Nicaragua 0.743 54 17 72 Turkey 0.751 71 1 73 Jordan 0.752 76 -3 74 Peru 0.753 74 0 75 Ecuador 0.758 68 7 76 Iraq 0.759 96 -20 77 United Arab Emir. 0.782 127 -50 78 Thailand 0.783 55 23 79 Paraguay 0.784 65 14 80 Brazil 0.784 85 -5 81 Mauritius 0.788 75 6 82 Korea, Dem. Rep. 0.789 67 15 83 Sri Lanka 0.789 38 45 84 Albania 0.790 61 23 85 Malaysia 0.800 80 5 86 Colombia 0.801 72 14 87 Jamaica 0.824 62 25 88 Kuwait 0.824 122 -34 89 Venezuela 0.861 95 -6 90 Romania 0.863 84 6 91 Mexico 0.876 81 10 92 Cuba 0.877 66 26 93 Panama 0.883 88 5 94 Trinidad/Tobago 0.885 100 -6 95 Portugal 0.899 94 1 96 Singapore 0.899 110 -14 97 Korea, Rep. 0.903 92 5 98 Poland 0.910 83 15 99 Argentina 0.910 89 10 100 Yugoslavia 0.913 90 10 101 Hungary 0.915 87 14 102 Uruguay 0.916 86 16 103 Costa Rica 0.916 77 26 104 Bulgaria 0.918 99 5 105 USSR 0.920 101 4 106 Czechoslovakia 0.931 102 4 107 Chile 0.931 73 34 108 Hong Kong 0.936 111 -3 109 Greece 0.946 98 11 110 German Dem. Rep. 0.953 115 -5 111 Israel 0.957 108 3 112 USA 0.961 129 -17 113 Austria 0.961 118 -5 114 Ireland 0.961 106 8 115 Spain 0.965 105 10 116 Belgium 0.966 116 0 117 Italy 0.966 112 5 118 New Zealand 0.966 109 9 119 Germany, Fed. R. 0.967 120 -1 120 Finland 0.967 121 -1 121 United Kingdom 0.970 113 8 122 Denmark 0.971 123 -1 123 France 0.974 119 4 124 Australia 0.978 114 10 125 Norway 0.983 128 -3 126 Canada 0.983 124 2 127 Netherlands 0.984 117 10 128 Switzerland 0.986 130 -2 129 Sweden 0.987 125 4 130 Japan 0.996 126 4 [From 5 September "Mennonite Weekly Review"] Posted on Activist's Mailing List (ACTIV-L@UMCVMB) which should legally place the data in the public domain, right? Copied from there and contributed by Tim Arnold (arnold@stat.ncsu.edu) ================================================================ Human Development Index [INFO] United Nations Development Program compiled an Index of Human Development. Information file companion to the DATA file. ============================================================================ To measure the quality of life in a nation, the United Nations Development Program started figuring a Human Development Index. A nation's HDI is composed of life expectancy, adult literacy and Gross National Product per capita. By combining these three elements and by pitting each nation's indicators against "the best," we come up with a worldwide HDI. Comparing the HDI rating with the traditional GNP per capita rating reveals some poor countries' remarkable progress in human development. These countries got more bang for their development buck by giving their aid to the most needy people. The comparison also shows that some countries, including the U.S., did not translate their wealth into social benefits. In the HDI rankings, the Arab and Moslem countries come out poorly, mainly because of low literacy among women. The formerly communist countries come out rather well because literacy is a priority and their GNP is generally low. Latin America comes out with many plusses because their GNPs are low while they still enjoy the higher literacy and improved health-care investments of earlier years. Africa is a mixed lot. Some oil exporters, such as Angola, Gabon, Cameroon and the Congo, did not translate their wealth into social benefits. Others--Tanzania, Madagascar, Zambia, which have poorly managed economies--were still able to improve their people's health and schooling. Among the wealthier countries, the physical and educational benefits generally kept pace with improved economies. An exception is the U.S., where the economy flourished in the '80s but social services stagnated and declined. The chart below lists the world's countries according to their Human Development Index--a measure of quality of life based on life expectancy, adult literacy and Gross National Product per capita. The nations are ranked from lowest quality of life to highest. The "HDI rank minus GNP rank column measures how well the nations translate the wealth they have into benefits for their citizens. A positive number in this column indicates the country makes good use of its resources to help its people. A negative number indicates it does not. ======================================================================= Posted on Activist's Mailing List (ACTIV-L@UMCVMB) which should legally place the data in the public domain, right? Copied from there and contributed by Tim Arnold (arnold@stat.ncsu.edu) Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

2 features

hdi (target)numeric112 unique values
0 missing
rank (row identifier)numeric130 unique values
0 missing
country (ignore)nominal130 unique values
0 missing
gnp_per_capitanumeric130 unique values
0 missing
Show all 2 features

107 properties

NumberOfInstances
130
Number of instances (rows) of the dataset.
NumberOfFeatures
2
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
2
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
AutoCorrelation
0.99
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
-1.2
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
65.5
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
37.67
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-1.23
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
33.07
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0.18
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
18.97
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.26
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.64
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.35
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.27
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.26
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.64
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.35
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.27
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
33.07
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.18
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
18.97
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-1.2
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
65.5
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
37.67
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Regression on humandevel
2 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: hdi
Supervised Regression on humandevel
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: hdi
Clustering on humandevel
0 runs
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
Clustering on humandevel
0 runs - estimation_procedure: 50 times Clustering
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