Data
dermatology-6

dermatology-6

in_preparation ARFF Publicly available Visibility: public Uploaded 21-11-2018 by Andriy Mulyar
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  • IR_16.9 keel-imbalanced study_184
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An imbalanced version of the Dermatology data set. where the possitive examples belong to the class 6 and the negative examples to the rest of the classes. (IR: 16.9)

35 features

class (target)string2 unique values
0 missing
Erythemanumeric4 unique values
0 missing
Scalingnumeric4 unique values
0 missing
Definite_bordersnumeric4 unique values
0 missing
Itchingnumeric4 unique values
0 missing
Koebner_phenomenonnumeric4 unique values
0 missing
Polygonal_papulesnumeric4 unique values
0 missing
Follicular_papulesnumeric4 unique values
0 missing
Oral_mucosalnumeric4 unique values
0 missing
Knee_and_elbownumeric4 unique values
0 missing
Scalp_involvementnumeric4 unique values
0 missing
Family_historynumeric2 unique values
0 missing
Melanin_incontinencenumeric4 unique values
0 missing
Eosinophilsnumeric3 unique values
0 missing
PNL_infiltratenumeric4 unique values
0 missing
Fibrosisnumeric4 unique values
0 missing
Exocytosisnumeric4 unique values
0 missing
Acanthosisnumeric4 unique values
0 missing
Hyperkeratosisnumeric4 unique values
0 missing
Parakeratosisnumeric4 unique values
0 missing
Clubbingnumeric4 unique values
0 missing
Elongationnumeric4 unique values
0 missing
Thinningnumeric4 unique values
0 missing
Spongiform_pustulenumeric4 unique values
0 missing
Munro_microabcessnumeric4 unique values
0 missing
Focal_hypergranulosisnumeric4 unique values
0 missing
Granular_layernumeric4 unique values
0 missing
Vacuolisationnumeric4 unique values
0 missing
Spongiosisnumeric4 unique values
0 missing
Saw-tooth_appearancenumeric4 unique values
0 missing
Follicular_horn_plugnumeric4 unique values
0 missing
Perifollicular_parakeratosisnumeric4 unique values
0 missing
Inflammatory_monoluclearnumeric4 unique values
0 missing
Band-like_infiltratenumeric4 unique values
0 missing
Agenumeric60 unique values
0 missing

62 properties

358
Number of instances (rows) of the dataset.
35
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.
34
Number of numeric attributes.
0
Number of nominal attributes.
1.3
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
97.14
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.38
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
1.97
Third quartile of skewness among attributes of the numeric type.
4.72
Maximum skewness among attributes of the numeric type.
0.33
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1
Third quartile of standard deviation of attributes of the numeric type.
15.32
Maximum standard deviation of attributes of the numeric type.
5.59
Percentage of instances belonging to the least frequent class.
-0.22
First quartile of kurtosis among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
20
Number of instances belonging to the least frequent class.
0.38
First quartile of means among attributes of the numeric type.
2.45
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1.79
Mean of means among attributes of the numeric type.
0.1
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.7
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
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.
0.1
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.61
Second quartile (Median) of kurtosis 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.
1.4
Mean skewness among attributes of the numeric type.
0.54
Second quartile (Median) of means among attributes of the numeric type.
94.41
Percentage of instances belonging to the most frequent class.
1.26
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
338
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.35
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.41
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.87
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
22.85
Maximum kurtosis among attributes of the numeric type.
0.11
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
2.49
Third quartile of kurtosis among attributes of the numeric type.
36.3
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

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