Data
covertype

covertype

active ARFF Publicly available Visibility: public Uploaded 22-06-2015 by Joaquin Vanschoren
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Author: Jock A. Blackard, Dr. Denis J. Dean, Dr. Charles W. Anderson Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Covertype) - 1998 This is the original version of the famous covertype dataset in ARFF format. Covertype Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types). This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices. Some background information for these four wilderness areas: Neota (area 2) probably has the highest mean elevational value of the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3) would have a lower mean elevational value, while Cache la Poudre (area 4) would have the lowest mean elevational value. As for primary major tree species in these areas, Neota would have spruce/fir (type 1), while Rawah and Comanche Peak would probably have lodgepole pine (type 2) as their primary species, followed by spruce/fir and aspen (type 5). Cache la Poudre would tend to have Ponderosa pine (type 3), Douglas-fir (type 6), and cottonwood/willow (type 4). The Rawah and Comanche Peak areas would tend to be more typical of the overall dataset than either the Neota or Cache la Poudre, due to their assortment of tree species and range of predictive variable values (elevation, etc.) Cache la Poudre would probably be more unique than the others, due to its relatively low elevation range and species composition. Attribute Information: Given is the attribute name, attribute type, the measurement unit and a brief description. The forest cover type is the classification problem. The order of this listing corresponds to the order of numerals along the rows of the database. > Name / Data Type / Measurement / Description Elevation / quantitative /meters / Elevation in meters Aspect / quantitative / azimuth / Aspect in degrees azimuth Slope / quantitative / degrees / Slope in degrees Horizontal_Distance_To_Hydrology / quantitative / meters / Horz Dist to nearest surface water features Vertical_Distance_To_Hydrology / quantitative / meters / Vert Dist to nearest surface water features Horizontal_Distance_To_Roadways / quantitative / meters / Horz Dist to nearest roadway Hillshade_9am / quantitative / 0 to 255 index / Hillshade index at 9am, summer solstice Hillshade_Noon / quantitative / 0 to 255 index / Hillshade index at noon, summer solstice Hillshade_3pm / quantitative / 0 to 255 index / Hillshade index at 3pm, summer solstice Horizontal_Distance_To_Fire_Points / quantitative / meters / Horz Dist to nearest wildfire ignition points Wilderness_Area (4 binary columns) / qualitative / 0 (absence) or 1 (presence) / Wilderness area designation Soil_Type (40 binary columns) / qualitative / 0 (absence) or 1 (presence) / Soil Type designation Cover_Type (7 types) / integer / 1 to 7 / Forest Cover Type designation Relevant Papers: - Blackard, Jock A. and Denis J. Dean. 2000. "Comparative Accuracies of Artificial Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables." Computers and Electronics in Agriculture 24(3):131-151. - Blackard, Jock A. and Denis J. Dean. 1998. "Comparative Accuracies of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables." Second Southern Forestry GIS Conference. University of Georgia. Athens, GA. Pages 189-199. - Blackard, Jock A. 1998. "Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types." Ph.D. dissertation. Department of Forest Sciences. Colorado State University. Fort Collins, Colorado. 165 pages.

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.
2959.37
Maximum of means 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.
0.21
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
18.18
Percentage of numeric attributes.
2072.76
Third quartile of means among attributes of the numeric type.
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.
-1.18
Minimum skewness among attributes of the numeric type.
81.82
Percentage of nominal attributes.
1.18
Third quartile of skewness among attributes of the numeric type.
1.79
Maximum skewness among attributes of the numeric type.
7.49
Minimum standard deviation of attributes of the numeric type.
0.02
First quartile of entropy among attributes.
541.04
Third quartile of standard deviation of attributes of the numeric type.
1559.25
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.
0.75
Standard deviation of the number of distinct values among attributes of the nominal type.
0.18
Average entropy of the attributes.
2747
Number of instances belonging to the least frequent class.
118.5
First quartile of means among attributes of the numeric 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.
835.34
Mean of means among attributes of the numeric type.
-0.88
First quartile of skewness among attributes of the numeric type.
0.95
Average class difference between consecutive instances.
0.02
Average mutual information between the nominal attributes and the target attribute.
25.02
First quartile of standard deviation of attributes of the numeric type.
1.74
Entropy of the target attribute values.
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.
0
Number of attributes divided by the number of instances.
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.
80.19
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.28
Mean skewness among attributes of the numeric type.
217.73
Second quartile (Median) of means among attributes of the numeric type.
48.76
Percentage of instances belonging to the most frequent class.
363.85
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.
283301
Number 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.
0.99
Maximum entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
80
Percentage of binary attributes.
85.1
Second quartile (Median) of standard deviation of attributes of the numeric type.
5.25
Maximum kurtosis among attributes of the numeric type.
14.1
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.29
Third quartile of entropy among attributes.

13 tasks

1 runs - estimation_procedure: 33% Holdout set - 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 Crossvalidation - 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: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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