Data
covertype

covertype

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Author: Jock A. Blackard, Dr. Denis J. Dean, Dr. Charles W. Anderson Source: Unknown Note: This is a 19% subsample from the original UCI dataset (see version 4). The origin is currently not clear. 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
elevationnumeric1769 unique values
0 missing
aspectnumeric361 unique values
0 missing
slopenumeric57 unique values
0 missing
horizontal_distance_to_hydrologynumeric487 unique values
0 missing
Vertical_Distance_To_Hydrologynumeric562 unique values
0 missing
Horizontal_Distance_To_Roadwaysnumeric5343 unique values
0 missing
Hillshade_9amnumeric195 unique values
0 missing
Hillshade_Noonnumeric155 unique values
0 missing
Hillshade_3pmnumeric253 unique values
0 missing
Horizontal_Distance_To_Fire_Pointsnumeric5276 unique values
0 missing
wilderness_area1numeric2 unique values
0 missing
wilderness_area2numeric2 unique values
0 missing
wilderness_area3numeric2 unique values
0 missing
wilderness_area4numeric2 unique values
0 missing
soil_type_1nominal2 unique values
0 missing
soil_type_2nominal2 unique values
0 missing
soil_type_3nominal2 unique values
0 missing
soil_type_4nominal2 unique values
0 missing
soil_type_5nominal2 unique values
0 missing
soil_type_6nominal2 unique values
0 missing
soil_type_7nominal2 unique values
0 missing
soil_type_8nominal2 unique values
0 missing
soil_type_9nominal2 unique values
0 missing
soil_type_10nominal2 unique values
0 missing
soil_type_11nominal2 unique values
0 missing
soil_type_12nominal2 unique values
0 missing
soil_type_13nominal2 unique values
0 missing
soil_type_14nominal2 unique values
0 missing
soil_type_15nominal2 unique values
0 missing
soil_type_16nominal2 unique values
0 missing
soil_type_17nominal2 unique values
0 missing
soil_type_18nominal2 unique values
0 missing
soil_type_19nominal2 unique values
0 missing
soil_type_20nominal2 unique values
0 missing
soil_type_21nominal2 unique values
0 missing
soil_type_22nominal2 unique values
0 missing
soil_type_23nominal2 unique values
0 missing
soil_type_24nominal2 unique values
0 missing
soil_type_25nominal2 unique values
0 missing
soil_type_26nominal2 unique values
0 missing
soil_type_27nominal2 unique values
0 missing
soil_type_28nominal2 unique values
0 missing
soil_type_29nominal2 unique values
0 missing
soil_type_30nominal2 unique values
0 missing
soil_type_31nominal2 unique values
0 missing
soil_type_32nominal2 unique values
0 missing
soil_type_33nominal2 unique values
0 missing
soil_type_34nominal2 unique values
0 missing
soil_type_35nominal2 unique values
0 missing
soil_type_36nominal2 unique values
0 missing
soil_type_37nominal2 unique values
0 missing
soil_type_38nominal2 unique values
0 missing
soil_type_39nominal2 unique values
0 missing
soil_type_40nominal2 unique values
0 missing

107 properties

110393
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.
14
Number of numeric attributes.
41
Number of nominal attributes.
0.06
Second quartile (Median) of entropy among attributes.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.46
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
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
46.82
Percentage of instances belonging to the most frequent class.
259.78
Mean standard deviation of attributes of the numeric type.
1.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.87
Entropy of the target attribute values.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
51682
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
148.95
Second quartile (Median) of means among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.72
Maximum entropy among attributes.
-1.95
Minimum kurtosis among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.53
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
14.48
Maximum kurtosis among attributes of the numeric type.
0.05
Minimum of means among attributes of the numeric type.
0.56
Second quartile (Median) of skewness among attributes of the numeric type.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
2957.98
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
72.73
Percentage of binary attributes.
32.56
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.08
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
0.22
Third quartile of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
148.97
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
7
The maximum number of distinct values among attributes of the nominal type.
-1.2
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
2.71
Third quartile of kurtosis among attributes of the numeric type.
0.39
Average class difference between consecutive instances.
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.06
Maximum skewness among attributes of the numeric type.
0.22
Minimum standard deviation of attributes of the numeric type.
25.45
Percentage of numeric attributes.
697.14
Third quartile of means among attributes of the numeric type.
0.8
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
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1558.39
Maximum standard deviation of attributes of the numeric type.
1.21
Percentage of instances belonging to the least frequent class.
74.55
Percentage of nominal attributes.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.14
Average entropy of the attributes.
1339
Number of instances belonging to the least frequent class.
0.02
First quartile of entropy among attributes.
1.4
Third quartile of skewness among attributes of the numeric type.
0.46
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
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.35
Mean kurtosis among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.59
First quartile of kurtosis among attributes of the numeric type.
229.24
Third quartile of standard deviation of attributes of the numeric type.
0.8
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
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
596.3
Mean of means among attributes of the numeric type.
0.38
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.44
First quartile of means among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.71
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.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.22
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.46
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
0.61
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
9.98
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
40
Number of binary attributes.
-0.41
First quartile of skewness among attributes of the numeric type.
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.8
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
0.78
Standard deviation of the number of distinct values among attributes of the nominal type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.12
Average number of distinct values among the attributes of the nominal type.
0.5
First quartile of standard deviation of attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.77
Mean skewness among attributes of the numeric type.

15 tasks

159 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
2 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
7 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
48 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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