Data
white-clover

white-clover

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
0 likes downloaded by 5 people , 5 total downloads 0 issues 0 downvotes
  • mythbusting_1 study_1 study_144 study_15 study_20 study_41
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

32 features

binaryClass (target)nominal2 unique values
0 missing
stratanominal7 unique values
0 missing
plotnominal3 unique values
0 missing
paddocknominal3 unique values
0 missing
WhiteClover-91numeric51 unique values
0 missing
BareGround-91numeric32 unique values
0 missing
Cocksfoot-91numeric50 unique values
0 missing
OtherGrasses-91numeric55 unique values
0 missing
OtherLegumes-91numeric36 unique values
0 missing
RyeGrass-91numeric58 unique values
0 missing
Weeds-91numeric51 unique values
0 missing
WhiteClover-92numeric51 unique values
0 missing
BareGround-92numeric31 unique values
0 missing
Cocksfoot-92numeric51 unique values
0 missing
OtherGrasses-92numeric42 unique values
0 missing
OtherLegumes-92numeric32 unique values
0 missing
RyeGrass-92numeric51 unique values
0 missing
Weeds-92numeric44 unique values
0 missing
WhiteClover-93numeric50 unique values
0 missing
BareGround-93numeric20 unique values
0 missing
Cocksfoot-93numeric53 unique values
0 missing
OtherGrasses-93numeric50 unique values
0 missing
OtherLegumes-93numeric40 unique values
0 missing
RyeGrass-93numeric52 unique values
0 missing
Weeds-93numeric51 unique values
0 missing
BareGround-94numeric23 unique values
0 missing
Cocksfoot-94numeric50 unique values
0 missing
OtherGrasses-94numeric45 unique values
0 missing
OtherLegumes-94numeric42 unique values
0 missing
RyeGrass-94numeric50 unique values
0 missing
Weeds-94numeric49 unique values
0 missing
strata-combinednominal3 unique values
0 missing

107 properties

63
Number of instances (rows) of the dataset.
32
Number of attributes (columns) of the dataset.
2
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.
27
Number of numeric attributes.
5
Number of nominal attributes.
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
30.52
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
9.4
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.51
Number of attributes divided by the number of instances.
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.
3.13
Percentage of binary attributes.
2.5
Third quartile of entropy among attributes.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
10.34
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.
-0.44
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
4.78
Third quartile of kurtosis among attributes of the numeric type.
0.66
Average class difference between consecutive instances.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.09
Maximum skewness among attributes of the numeric type.
1.09
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
19.67
Third quartile of means among attributes of the numeric type.
0.63
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
20.71
Maximum standard deviation of attributes of the numeric type.
39.68
Percentage of instances belonging to the least frequent class.
84.38
Percentage of numeric attributes.
0.18
Third quartile of mutual information between the nominal attributes and the target attribute.
0.41
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.84
Average entropy of the attributes.
25
Number of instances belonging to the least frequent class.
15.63
Percentage of nominal attributes.
2.1
Third quartile of skewness among attributes of the numeric type.
0.19
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
5.17
Mean kurtosis among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.43
First quartile of entropy among attributes.
13.41
Third quartile of standard deviation of attributes of the numeric type.
0.63
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
13.59
Mean of means among attributes of the numeric type.
0.32
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.32
First quartile of kurtosis among attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.41
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.09
Average mutual information between the nominal attributes and the target attribute.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
5.65
First quartile of means among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.19
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
18.63
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.63
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
1.95
Standard deviation of the number of distinct values among attributes of the nominal type.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.6
Average number of distinct values among the attributes of the nominal type.
0.35
First quartile of skewness among attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.41
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.28
Mean skewness among attributes of the numeric type.
5.38
First quartile of standard deviation of attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.19
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
0.32
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
60.32
Percentage of instances belonging to the most frequent class.
9.67
Mean standard deviation of attributes of the numeric type.
1.58
Second quartile (Median) of entropy among attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Entropy of the target attribute values.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
38
Number of instances belonging to the most frequent class.
1.38
Minimal entropy among attributes.
0.96
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.81
Maximum entropy among attributes.
-1.04
Minimum kurtosis among attributes of the numeric type.
13.8
Second quartile (Median) of means among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.44
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
42.42
Maximum kurtosis among attributes of the numeric type.
0.43
Minimum of means among attributes of the numeric type.
0.08
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

5 tasks

527 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
205 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
Define a new task