Data
fertility

fertility

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael G. Mantovani
0 likes downloaded by 7 people , 8 total downloads 0 issues 0 downvotes
  • mf_less_than_80 study_123 study_127 study_50 study_52 study_7 study_88
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: David Gil, Jose Luis Girela Source: UCI Please cite: David Gil, Jose Luis Girela, Joaquin De Juan, M. Jose Gomez-Torres, and Magnus Johnsson. Predicting seminal quality with artificial intelligence methods. Expert Systems with Applications, 39(16):12564 - 12573, 2012 Source: David Gil, dgil '@' dtic.ua.es, Lucentia Research Group, Department of Computer Technology, University of Alicante Jose Luis Girela, girela '@' ua.es, Department of Biotechnology, University of Alicante Attribute Information: Season in which the analysis was performed. 1) winter, 2) spring, 3) Summer, 4) fall. (-1, -0.33, 0.33, 1) Age at the time of analysis. 18-36 (0, 1) Childish diseases (ie , chicken pox, measles, mumps, polio) 1) yes, 2) no. (0, 1) Accident or serious trauma 1) yes, 2) no. (0, 1) Surgical intervention 1) yes, 2) no. (0, 1) High fevers in the last year 1) less than three months ago, 2) more than three months ago, 3) no. (-1, 0, 1) Frequency of alcohol consumption 1) several times a day, 2) every day, 3) several times a week, 4) once a week, 5) hardly ever or never (0, 1) Smoking habit 1) never, 2) occasional 3) daily. (-1, 0, 1) Number of hours spent sitting per day ene-16 (0, 1) Output: Diagnosis normal (N), altered (O)

10 features

Class (target)nominal2 unique values
0 missing
V1numeric4 unique values
0 missing
V2numeric18 unique values
0 missing
V3numeric2 unique values
0 missing
V4numeric2 unique values
0 missing
V5numeric2 unique values
0 missing
V6numeric3 unique values
0 missing
V7numeric5 unique values
0 missing
V8numeric3 unique values
0 missing
V9numeric14 unique values
0 missing

62 properties

100
Number of instances (rows) of the dataset.
10
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.
9
Number of numeric attributes.
1
Number of nominal attributes.
-0.44
First quartile of skewness among attributes of the numeric type.
0.39
Mean of means among attributes of the numeric type.
0.18
First quartile of standard deviation of attributes of the numeric type.
0.78
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.53
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.
-0.25
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.1
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.44
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.
-0.04
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
88
Percentage of instances belonging to the most frequent class.
0.44
Mean standard deviation of attributes of the numeric type.
0.25
Second quartile (Median) of skewness among attributes of the numeric type.
88
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
10
Percentage of binary attributes.
0.5
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-2.04
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
3.05
Maximum kurtosis among attributes of the numeric type.
-0.35
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
0.66
Third quartile of kurtosis among attributes of the numeric type.
0.87
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
90
Percentage of numeric attributes.
0.75
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
10
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-2.23
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.7
Third quartile of skewness among attributes of the numeric type.
0.78
Maximum skewness among attributes of the numeric type.
0.12
Minimum standard deviation of attributes of the numeric type.
-1.71
First quartile of kurtosis among attributes of the numeric type.
0.69
Third quartile of standard deviation of attributes of the numeric type.
0.81
Maximum standard deviation of attributes of the numeric type.
12
Percentage of instances belonging to the least frequent class.
12
Number of instances belonging to the least frequent class.
0.06
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.
Average entropy of the attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.27
Mean kurtosis among attributes of the numeric type.

5 tasks

419 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task