Data
covertype

covertype

active ARFF Publicly available Visibility: public Uploaded 10-04-2014 by Jan van Rijn
1 likes downloaded by 39 people , 48 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: Normalized version of the Forest Covertype dataset (see version 1), so that the numerical values are between 0 and 1. Contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.

55 features

class (target)nominal7 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

62 properties

581012
Number of instances (rows) of the dataset.
55
Number of attributes (columns) of the dataset.
7
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.
10
Number of numeric attributes.
45
Number of nominal attributes.
7.52
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.09
Second quartile (Median) of entropy among attributes.
1.74
Entropy of the target attribute values.
2.11
Average number of distinct values among the attributes of the nominal type.
1.06
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
0.28
Mean skewness among attributes of the numeric type.
0.38
Second quartile (Median) of means among attributes of the numeric type.
80.19
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.15
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
48.76
Percentage of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
0.56
Second quartile (Median) of skewness among attributes of the numeric type.
283301
Number of instances belonging to the most frequent class.
-1.22
Minimum kurtosis among attributes of the numeric type.
80
Percentage of binary attributes.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.99
Maximum entropy among attributes.
0.19
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.29
Third quartile of entropy among attributes.
5.25
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
1.92
Third quartile of kurtosis among attributes of the numeric type.
0.88
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
18.18
Percentage of numeric attributes.
0.63
Third quartile of means among attributes of the numeric type.
0.21
Maximum mutual information between the nominal attributes and the target attribute.
-1.18
Minimum skewness among attributes of the numeric type.
81.82
Percentage of nominal attributes.
0.03
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
1.79
Maximum skewness among attributes of the numeric type.
0.08
Minimum standard deviation of attributes of the numeric type.
0.02
First quartile of entropy among attributes.
1.18
Third quartile of skewness among attributes of the numeric type.
0.31
Maximum standard deviation of attributes of the numeric type.
0.47
Percentage of instances belonging to the least frequent class.
0.2
First quartile of kurtosis among attributes of the numeric type.
0.19
Third quartile of standard deviation of attributes of the numeric type.
0.18
Average entropy of the attributes.
2747
Number of instances belonging to the least frequent class.
0.26
First quartile of means among attributes of the numeric type.
0.75
Standard deviation of the number of distinct values among attributes of the nominal type.
1.23
Mean kurtosis among attributes of the numeric type.
44
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.46
Mean of means among attributes of the numeric type.
-0.88
First quartile of skewness among attributes of the numeric type.
0.02
Average mutual information between the nominal attributes and the target attribute.
0.1
First quartile of standard deviation of attributes of the numeric type.
0.95
Average class difference between consecutive instances.

17 tasks

43 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
2 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
2 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
2 runs - estimation_procedure: 4-fold Crossvalidation - 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
2 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
290 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task