Data
AP_Ovary_Kidney

AP_Ovary_Kidney

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC curve, etc.) for classification, feature selection or clustering algorithms. This repository was inspired by an increasing need in machine learning / bioinformatics communities for a collection of microarray classification problems that could be used by different researches. This way many different classification or feature selection techniques can finally be compared to eachother on the same set of problems. Origin of data Each gene expression sample in GEMLeR repository comes from a large publicly available expO (Expression Project For Oncology) repository by International Genomics Consortium. The goal of expO and its consortium supporters is to procure tissue samples under standard conditions and perform gene expression analyses on a clinically annotated set of deidentified tumor samples. The tumor data is updated with clinical outcomes and is released into the public domain without intellectual property restriction. The availability of this information translates into direct benefits for patients, researchers and pharma alike. Source: expO website Although there are various other sources of gene expression data available, a decision to use data from expO repository was made because of: - consistency of tissue samples processing procedure - same microarray platform used for all samples - availability of additional information for combined genotype-phenotype studies - availability of a large number of samples for different tumor types In case of publishing material based on GEMLeR datasets, then, please note the assistance you received by using this repository. This will help others to obtain the same datasets and replicate your experiments. Please cite as follows when referring to this repository: Stiglic, G., & Kokol, P. (2010). Stability of Ranked Gene Lists in Large Microarray Analysis Studies. Journal of biomedicine biotechnology, 2010, 616358. You are also welcome to acknowledge the contribution of expO (Expression Project For Oncology) and International Genomics Consortium for providing their gene expression samples to the public.

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Show first 100 features

107 properties

NumberOfInstances
458
Number of instances (rows) of the dataset.
NumberOfFeatures
10936
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
10935
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
REPTreeDepth3Kappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
128601.8
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
2.21
Second quartile (Median) of skewness among attributes of the numeric type.
RandomTreeDepth1AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
23.88
Number of attributes divided by the number of instances.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
0.01
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
961.77
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.23
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
RandomTreeDepth1Kappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
21.39
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
469.63
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
24.78
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.96
Average class difference between consecutive instances.
RandomTreeDepth2AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
257331.42
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
43.23
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
99.99
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
2747.58
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
0.93
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
RandomTreeDepth2ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
198
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
0.01
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
0.07
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
RandomTreeDepth2Kappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
31.28
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
3.88
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
0.86
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
RandomTreeDepth3AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
3361.59
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
3.58
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1812.36
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
0.93
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
RandomTreeDepth3ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
924.93
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
0.07
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
RandomTreeDepth3Kappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
0.86
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
0.93
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
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
1.46
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NErrRate
0.07
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
kNN1NAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
3.43
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
639.83
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
0.86
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
kNN1NErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
56.77
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
2099.91
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
0.99
Entropy of the target attribute values.
kNN1NKappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
260
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
8.27
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.09
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1534.89
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
457.79
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
49.52
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Show all 107 properties

15 tasks

Supervised Classification on AP_Ovary_Kidney
66 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Tissue
Supervised Classification on AP_Ovary_Kidney
1 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Tissue
Supervised Classification on AP_Ovary_Kidney
0 runs - estimation_procedure: 33% Holdout set - target_feature: Tissue
Supervised Data Stream Classification on AP_Ovary_Kidney
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Tissue
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
Clustering on AP_Ovary_Kidney
0 runs - estimation_procedure: 50 times Clustering
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