Data
white-clover

white-clover

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • mythbusting_1 study_1 study_144 study_15 study_20 study_41
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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

19 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.
60.32
Percentage of instances belonging to the most frequent class.
15.63
Percentage of nominal attributes.
38
Number of instances belonging to the most frequent class.
39.68
Percentage of instances belonging to the least frequent class.
25
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
3.13
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.66
Average class difference between consecutive instances.
0
Percentage of missing values.
0.51
Number of attributes divided by the number of instances.
84.38
Percentage of numeric attributes.

5 tasks

544 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
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