Data
KungChi3

KungChi3

active ARFF Publicly available Visibility: public Uploaded 19-05-2015 by Hans Bauer
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Author: Hans Bauer Jesus","Deter Bergman Source: Unknown - Date unknown Please cite: Kung chi

40 features

def (target)nominal2 unique values
0 missing
anumeric20 unique values
0 missing
bnumeric23 unique values
0 missing
cnumeric27 unique values
0 missing
dnumeric3 unique values
0 missing
enumeric12 unique values
0 missing
fnumeric21 unique values
0 missing
gnumeric15 unique values
0 missing
hnumeric27 unique values
0 missing
inumeric13 unique values
0 missing
jnumeric12 unique values
0 missing
knumeric16 unique values
0 missing
lnumeric18 unique values
0 missing
mnumeric56 unique values
0 missing
nnumeric9 unique values
0 missing
onumeric16 unique values
0 missing
pnumeric49 unique values
0 missing
rnumeric4 unique values
0 missing
snumeric16 unique values
0 missing
tnumeric19 unique values
0 missing
unumeric122 unique values
0 missing
vnumeric108 unique values
0 missing
znumeric122 unique values
0 missing
aanumeric52 unique values
0 missing
abnumeric95 unique values
0 missing
acnumeric19 unique values
0 missing
adnumeric122 unique values
0 missing
aenumeric121 unique values
0 missing
afnumeric28 unique values
0 missing
agnumeric13 unique values
0 missing
ahnumeric21 unique values
0 missing
ainumeric51 unique values
0 missing
ajnumeric29 unique values
0 missing
aknumeric70 unique values
0 missing
alnumeric83 unique values
0 missing
amnumeric48 unique values
0 missing
annumeric20 unique values
0 missing
aonumeric56 unique values
0 missing
apnumeric41 unique values
0 missing
arnumeric51 unique values
0 missing

18 properties

123
Number of instances (rows) of the dataset.
40
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.
39
Number of numeric attributes.
1
Number of nominal attributes.
2.5
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.87
The predictive accuracy obtained by always predicting the majority class.
97.5
Percentage of numeric attributes.
0.33
Number of attributes divided by the number of instances.
2.5
Percentage of nominal attributes.
86.99
Percentage of instances belonging to the most frequent class.
107
Number of instances belonging to the most frequent class.
16
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

3 tasks

1 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: matthews_correlation_coefficient - target_feature: def
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