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house_16H

house_16H

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Felicia West
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  • binarized_regression_problem mythbusting_1 study_1 study_15 study_20 study_41 study_7 study_293
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

17 features

binaryClass (target)nominal2 unique values
0 missing
P18p2numeric5784 unique values
0 missing
H40p4numeric2421 unique values
0 missing
H18pAnumeric9001 unique values
0 missing
H13p1numeric17010 unique values
0 missing
H10p1numeric10194 unique values
0 missing
H8p2numeric10420 unique values
0 missing
H2p2numeric15375 unique values
0 missing
P27p4numeric11627 unique values
0 missing
P1numeric8832 unique values
0 missing
P16p2numeric15450 unique values
0 missing
P15p3numeric9160 unique values
0 missing
P15p1numeric18332 unique values
0 missing
P14p9numeric15882 unique values
0 missing
P11p4numeric18821 unique values
0 missing
P6p2numeric12287 unique values
0 missing
P5p1numeric16412 unique values
0 missing
Show all 17 features

107 properties

NumberOfInstances
22784
Number of instances (rows) of the dataset.
NumberOfFeatures
17
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
16
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.58
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.87
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
0.15
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
0.64
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
0.87
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
0.15
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
0.64
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
0.87
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
0.15
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
0.64
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
0.88
Entropy of the target attribute values.
DecisionStumpAUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
70.4
Percentage of instances belonging to the most frequent class.
MajorityClassSize
16040
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
7201.56
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
7808.67
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
72.75
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
65872.42
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
468.82
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
488.32
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
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
6.14
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
4117.12
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.1
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-5.87
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.01
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
29.6
Percentage of instances belonging to the least frequent class.
MinorityClassSize
6744
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
5.88
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
94.12
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
5.88
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
3.65
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.06
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
0.15
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.06
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
14.09
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.14
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
2.37
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.09
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
39.87
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.66
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
3.95
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.14
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

16 tasks

Supervised Classification on house_16H
390 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on house_16H
214 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on house_16H
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: binaryClass
Supervised Classification on house_16H
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: binaryClass
Supervised Data Stream Classification on house_16H
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
Clustering on house_16H
0 runs
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
Clustering on house_16H
0 runs - estimation_procedure: 50 times Clustering
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