Data
CovPokElec

CovPokElec

active ARFF Publicly available Visibility: public Uploaded 10-04-2014 by Jan van Rijn
0 likes downloaded by 26 people , 28 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Albert Bifet Source: [MOA](http://moa.cms.waikato.ac.nz/datasets/) - 2009 Please cite: Dataset created to study concept drift in stream mining. It is constructed by combining the Covertype, Poker-Hand, and Electricity datasets. More details can be found in: Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldà. 2009. New ensemble methods for evolving data streams. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09).

73 features

class (target)nominal10 unique values
0 missing
Elevationnumeric1978 unique values
0 missing
Aspectnumeric361 unique values
0 missing
Slopenumeric67 unique values
0 missing
Horizontal_Distance_To_Hydrologynumeric551 unique values
0 missing
Vertical_Distance_To_Hydrologynumeric700 unique values
0 missing
Horizontal_Distance_To_Roadwaysnumeric5785 unique values
0 missing
Hillshade_9amnumeric207 unique values
0 missing
Hillshade_Noonnumeric185 unique values
0 missing
Hillshade_3pmnumeric255 unique values
0 missing
Horizontal_Distance_To_Fire_Pointsnumeric5827 unique values
0 missing
Wilderness_Area1nominal2 unique values
0 missing
Wilderness_Area2nominal2 unique values
0 missing
Wilderness_Area3nominal2 unique values
0 missing
Wilderness_Area4nominal2 unique values
0 missing
Soil_Type1nominal2 unique values
0 missing
Soil_Type2nominal2 unique values
0 missing
Soil_Type3nominal2 unique values
0 missing
Soil_Type4nominal2 unique values
0 missing
Soil_Type5nominal2 unique values
0 missing
Soil_Type6nominal2 unique values
0 missing
Soil_Type7nominal2 unique values
0 missing
Soil_Type8nominal2 unique values
0 missing
Soil_Type9nominal2 unique values
0 missing
Soil_Type10nominal2 unique values
0 missing
Soil_Type11nominal2 unique values
0 missing
Soil_Type12nominal2 unique values
0 missing
Soil_Type13nominal2 unique values
0 missing
Soil_Type14nominal2 unique values
0 missing
Soil_Type15nominal2 unique values
0 missing
Soil_Type16nominal2 unique values
0 missing
Soil_Type17nominal2 unique values
0 missing
Soil_Type18nominal2 unique values
0 missing
Soil_Type19nominal2 unique values
0 missing
Soil_Type20nominal2 unique values
0 missing
Soil_Type21nominal2 unique values
0 missing
Soil_Type22nominal2 unique values
0 missing
Soil_Type23nominal2 unique values
0 missing
Soil_Type24nominal2 unique values
0 missing
Soil_Type25nominal2 unique values
0 missing
Soil_Type26nominal2 unique values
0 missing
Soil_Type27nominal2 unique values
0 missing
Soil_Type28nominal2 unique values
0 missing
Soil_Type29nominal2 unique values
0 missing
Soil_Type30nominal2 unique values
0 missing
Soil_Type31nominal2 unique values
0 missing
Soil_Type32nominal2 unique values
0 missing
Soil_Type33nominal2 unique values
0 missing
Soil_Type34nominal2 unique values
0 missing
Soil_Type35nominal2 unique values
0 missing
Soil_Type36nominal2 unique values
0 missing
Soil_Type37nominal2 unique values
0 missing
Soil_Type38nominal2 unique values
0 missing
Soil_Type39nominal2 unique values
0 missing
Soil_Type40nominal2 unique values
0 missing
s1nominal4 unique values
0 missing
r1numeric12 unique values
0 missing
s2nominal4 unique values
0 missing
r2numeric13 unique values
0 missing
s3nominal4 unique values
0 missing
r3numeric13 unique values
0 missing
s4nominal4 unique values
0 missing
r4numeric13 unique values
0 missing
s5nominal4 unique values
0 missing
r5numeric12 unique values
0 missing
datenumeric1 unique values
0 missing
daynominal1 unique values
0 missing
periodnumeric1 unique values
0 missing
nswpricenumeric1 unique values
0 missing
nswdemandnumeric1 unique values
0 missing
vicpricenumeric1 unique values
0 missing
vicdemandnumeric1 unique values
0 missing
transfernumeric1 unique values
0 missing

107 properties

1455525
Number of instances (rows) of the dataset.
73
Number of attributes (columns) of the dataset.
10
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
22
Number of numeric attributes.
51
Number of nominal attributes.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.13
Maximum standard deviation of attributes of the numeric type.
0
Percentage of instances belonging to the least frequent class.
30.14
Percentage of numeric attributes.
1.17
Third quartile of means among attributes of the numeric type.
0.68
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
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.28
Average entropy of the attributes.
2
Number of instances belonging to the least frequent class.
69.86
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
0.43
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
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4.19
Mean kurtosis among attributes of the numeric type.
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.01
First quartile of entropy among attributes.
1.98
Third quartile of skewness among attributes of the numeric type.
0.24
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
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.05
Mean of means among attributes of the numeric type.
0.94
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.02
First quartile of kurtosis among attributes of the numeric type.
0.54
Third quartile of standard deviation of attributes of the numeric type.
0.68
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
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Average mutual information between the nominal attributes and the target attribute.
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.14
First quartile of means among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.43
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
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
24.55
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
44
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.24
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
1.26
Standard deviation of the number of distinct values among attributes of the nominal type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.33
Average number of distinct values among the attributes of the nominal type.
-1.11
First quartile of skewness among attributes of the numeric type.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.68
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.27
Mean skewness among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.43
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
0.2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
44.97
Percentage of instances belonging to the most frequent class.
0.57
Mean standard deviation of attributes of the numeric type.
0.06
Second quartile (Median) of entropy among attributes.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.24
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
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
654548
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
3.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.59
Entropy of the target attribute values.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.72
Maximum entropy among attributes.
-1.44
Minimum kurtosis among attributes of the numeric type.
0.42
Second quartile (Median) of means among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.54
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
18.57
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
12.01
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.32
Second quartile (Median) of skewness among attributes of the numeric type.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Number of attributes divided by the number of instances.
0.08
Maximum mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
60.27
Percentage of binary attributes.
0.1
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
146.53
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
10
The maximum number of distinct values among attributes of the nominal type.
-2.67
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.18
Third quartile of entropy among attributes.
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.3
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
7.66
Third quartile of kurtosis among attributes of the numeric type.
0.83
Average class difference between consecutive instances.

16 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
331 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task