Data
fertility

fertility

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael Gomes Mantovani
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  • mf_less_than_80 study_123 study_127 study_50 study_52 study_7 study_88 study_236
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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

19 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.
10
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.78
Average class difference between consecutive instances.
0
Percentage of missing values.
0.1
Number of attributes divided by the number of instances.
90
Percentage of numeric attributes.
88
Percentage of instances belonging to the most frequent class.
10
Percentage of nominal attributes.
88
Number of instances belonging to the most frequent class.
12
Percentage of instances belonging to the least frequent class.
12
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

13 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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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