Data
CovPokElec

CovPokElec

active ARFF Publicly available Visibility: public Uploaded 10-04-2014 by Jan van Rijn
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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

19 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.
60.27
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.83
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
30.14
Percentage of numeric attributes.
44.97
Percentage of instances belonging to the most frequent class.
69.86
Percentage of nominal attributes.
654548
Number of instances belonging to the most frequent class.
0
Percentage of instances belonging to the least frequent class.
2
Number of instances belonging to the least frequent class.
44
Number of binary attributes.

25 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
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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task