Data
PizzaCutter3

PizzaCutter3

active ARFF Publicly available Visibility: public Uploaded 19-05-2015 by Hans Bauer
0 likes downloaded by 6 people , 6 total downloads 0 issues 0 downvotes
  • origin_unknown study_52 study_7
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Hans Bauer Jesus","Deter Bergman Source: Unknown - Date unknown Please cite: Pizza cutter 3

38 features

def (target)nominal2 unique values
0 missing
anumeric52 unique values
0 missing
bnumeric66 unique values
0 missing
cnumeric19 unique values
0 missing
dnumeric25 unique values
0 missing
enumeric57 unique values
0 missing
fnumeric66 unique values
0 missing
gnumeric50 unique values
0 missing
hnumeric76 unique values
0 missing
inumeric42 unique values
0 missing
jnumeric47 unique values
0 missing
knumeric31 unique values
0 missing
lnumeric75 unique values
0 missing
mnumeric116 unique values
0 missing
nnumeric25 unique values
0 missing
onumeric59 unique values
0 missing
pnumeric115 unique values
0 missing
rnumeric8 unique values
0 missing
snumeric929 unique values
0 missing
tnumeric700 unique values
0 missing
unumeric1017 unique values
0 missing
vnumeric129 unique values
0 missing
znumeric326 unique values
0 missing
aanumeric29 unique values
0 missing
abnumeric1014 unique values
0 missing
acnumeric877 unique values
0 missing
adnumeric80 unique values
0 missing
aenumeric48 unique values
0 missing
afnumeric65 unique values
0 missing
agnumeric95 unique values
0 missing
ahnumeric67 unique values
0 missing
ainumeric214 unique values
0 missing
ajnumeric242 unique values
0 missing
aknumeric113 unique values
0 missing
alnumeric38 unique values
0 missing
amnumeric160 unique values
0 missing
annumeric334 unique values
0 missing
aonumeric117 unique values
0 missing

62 properties

1043
Number of instances (rows) of the dataset.
38
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.
37
Number of numeric attributes.
1
Number of nominal attributes.
Average mutual information between the nominal attributes and the target attribute.
2.14
First quartile of standard deviation of attributes of the numeric type.
0.78
Average class difference between consecutive instances.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.53
Entropy of the target attribute values.
2
Average number of distinct values among the attributes of the nominal type.
115.66
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.04
Number of attributes divided by the number of instances.
9.46
Mean skewness among attributes of the numeric type.
10.61
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
11897.95
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
87.82
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
9.03
Second quartile (Median) of skewness among attributes of the numeric type.
916
Number of instances belonging to the most frequent class.
-1.15
Minimum kurtosis among attributes of the numeric type.
2.63
Percentage of binary attributes.
14.38
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
0.08
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
871.24
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
317.9
Third quartile of kurtosis among attributes of the numeric type.
41989.19
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
97.37
Percentage of numeric attributes.
28.46
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
0.04
Minimum skewness among attributes of the numeric type.
2.63
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
28.54
Maximum skewness among attributes of the numeric type.
0.06
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
14.98
Third quartile of skewness among attributes of the numeric type.
412208.56
Maximum standard deviation of attributes of the numeric type.
12.18
Percentage of instances belonging to the least frequent class.
8.81
First quartile of kurtosis among attributes of the numeric type.
50.64
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
127
Number of instances belonging to the least frequent class.
1.94
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
189.28
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1243.54
Mean of means among attributes of the numeric type.
2.35
First quartile of skewness among attributes of the numeric type.

6 tasks

97 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: matthews_correlation_coefficient - target_feature: def
91 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: def
0 runs - estimation_procedure: 33% Holdout set - target_feature: def
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task