Data
pyrim

pyrim

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: The task consists of Learning Quantitative Structure Activity Relationships (QSARs). The Inhibition of Dihydrofolate Reductase by Pyrimidines.The data are described in: King, Ross .D., Muggleton, Steven., Lewis, Richard. and Sternberg, Michael.J.E. Drug Design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Original source: ?. Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html Characteristics: 74 cases; 28 continuous variables

28 features

activity (target)numeric63 unique values
0 missing
p1_polarnumeric5 unique values
0 missing
p1_sizenumeric6 unique values
0 missing
p1_flexnumeric8 unique values
0 missing
p1_h_donernumeric3 unique values
0 missing
p1_h_acceptornumeric3 unique values
0 missing
p1_pi_donernumeric3 unique values
0 missing
p1_pi_acceptornumeric3 unique values
0 missing
p1_polarisablenumeric4 unique values
0 missing
p1_sigmanumeric5 unique values
0 missing
p2_polarnumeric6 unique values
0 missing
p2_sizenumeric6 unique values
0 missing
p2_flexnumeric7 unique values
0 missing
p2_h_donernumeric3 unique values
0 missing
p2_h_acceptornumeric3 unique values
0 missing
p2_pi_donernumeric3 unique values
0 missing
p2_pi_acceptornumeric3 unique values
0 missing
p2_polarisablenumeric3 unique values
0 missing
p2_sigmanumeric5 unique values
0 missing
p3_polarnumeric4 unique values
0 missing
p3_sizenumeric4 unique values
0 missing
p3_flexnumeric3 unique values
0 missing
p3_h_donernumeric2 unique values
0 missing
p3_h_acceptornumeric3 unique values
0 missing
p3_pi_donernumeric3 unique values
0 missing
p3_pi_acceptornumeric1 unique values
0 missing
p3_polarisablenumeric4 unique values
0 missing
p3_sigmanumeric4 unique values
0 missing

19 properties

74
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
0
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.
28
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.89
Average class difference between consecutive instances.
0
Percentage of missing values.
0.38
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

13 tasks

6 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: activity
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: activity
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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