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sylva_agnostic

sylva_agnostic

deactivated ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Felicia West
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Author: [Isabelle Guyon](isabelle@clopinet.com) Source: [Agnostic Learning vs. Prior Knowledge Challenge](http://www.agnostic.inf.ethz.ch) Please cite: None Dataset from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLVA, GINA, NOVA, HIVA, ADA). The purpose of the challenge was to check if the performance of domain-specific feature engineering (prior knowledge) can be met by algorithms that were trained on data without any domain-specific knowledge (agnostic). For the latter, the data was anonymised and preprocessed in a way that makes them uninterpretable. This dataset contains the agnostic (smashed) version of a data set from the Remote Sensing and GIS Program of Colorado State University for the time span June 2005 - September 2006. A Similar, raw and not-agnostic data set is termed __Covertype Dataset__ and can be found in the [UCI Database](https://archive.ics.uci.edu/ml/datasets/covertype). Modified by TunedIT (converted to ARFF format) ### Topic The task of SYLVA is to classify forest cover types. The forest cover type for 30 x 30 meter cells is obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. We brought it back to a two-class classification problem (classifying Ponderosa pine vs. everything else). The “agnostic data” consists in 216 input variables. Each pattern is composed of 4 records: 2 true records matching the target and 2 records picked at random. Thus ½ of the features are distracters. The “prior knowledge data” is identical to the “agnostic data”, except that the distracters are removed and the identity of the features is revealed. ### Description Data type: non-sparse Number of features: 216 Number of examples and check-sums: Pos_ex Neg_ex Tot_ex Check_sum Train 805 12281 13086 238271607.00 Valid 81 1228 1309 23817234.00 This dataset contains samples from both training and validation datasets. ### Source Original owners: Remote Sensing and GIS Program Department of Forest Sciences College of Natural Resources Colorado State University Fort Collins, CO 80523 (contact Jock A. Blackard, jblackard/wo_ftcol@fs.fed.us or Dr. Denis J. Dean, denis@cnr.colostate.edu) Jock A. Blackard USDA Forest Service 3825 E. Mulberry Fort Collins, CO 80524 USA jblackard/wo_ftcol@fs.fed.us

217 features

label (target)nominal2 unique values
0 missing
attr0numeric173 unique values
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attr1numeric2 unique values
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attr4numeric915 unique values
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attr207numeric2 unique values
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attr208numeric1 unique values
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0 missing
Show first 100 features

107 properties

NumberOfInstances
14395
Number of instances (rows) of the dataset.
NumberOfFeatures
217
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
216
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.88
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.97
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
0.01
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
0.92
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
0.97
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
0.01
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
0.92
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
0.97
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
0.01
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
0.92
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
0.33
Entropy of the target attribute values.
DecisionStumpAUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
93.85
Percentage of instances belonging to the most frequent class.
MajorityClassSize
13509
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
14395
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
879.13
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
119.98
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
312.04
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
560.67
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
84.29
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
13.35
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
28.4
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.97
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
-1.18
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
6.15
Percentage of instances belonging to the least frequent class.
MinorityClassSize
886
Number of instances belonging to the least frequent class.
NaiveBayesAUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
0.46
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.54
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.46
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
5.41
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0
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
2.62
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
36.96
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.02
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
6.24
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
274.69
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.1
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
16.63
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.3
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.67
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

25 tasks

Supervised Classification on sylva_agnostic
60368 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: label
Supervised Classification on sylva_agnostic
126 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: label
Supervised Classification on sylva_agnostic
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: label
Learning Curve on sylva_agnostic
88 runs - estimation_procedure: 10-fold Learning Curve - target_feature: label
Supervised Data Stream Classification on sylva_agnostic
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: label
Clustering on sylva_agnostic
0 runs - target_feature: label
Clustering on sylva_agnostic
0 runs
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on sylva_agnostic
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on sylva_agnostic
1304 runs - target_feature: label
Subgroup Discovery on sylva_agnostic
1303 runs - target_feature: label
Subgroup Discovery on sylva_agnostic
0 runs - target_feature: label
Subgroup Discovery on sylva_agnostic
0 runs - target_feature: label
Subgroup Discovery on sylva_agnostic
0 runs - target_feature: label
Subgroup Discovery on sylva_agnostic
0 runs - target_feature: label
Subgroup Discovery on sylva_agnostic
0 runs - target_feature: label
Subgroup Discovery on sylva_agnostic
0 runs - target_feature: label
Define a new task

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