Data
triazines

triazines

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: The problem is to learn a regression equation/rule/tree to predict the activity from the descriptive structural attributes. The data and methodology is described in detail in: - King, Ross .D., Hurst, Jonathan. D., and Sternberg, Michael.J.E. A comparison of artificial intelligence methods for modelling QSARs Applied Artificial Intelligence, 1994 (in press). - Hurst, Jonathan. D., King, Ross .D. and Sternberg, Michael.J.E. Quantitative Structure-Activity Relationships by neural networks and inductive logic programming: 2. The inhibition of dihydrofolate reductase by triazines. Journal of Computer Aided Molecular Design, 1994 (in press). 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: 186 cases; 61 continuous variables

61 features

activity (target)numeric102 unique values
0 missing
p1_polarnumeric6 unique values
0 missing
p1_sizenumeric7 unique values
0 missing
p1_flexnumeric7 unique values
0 missing
p1_h_donernumeric3 unique values
0 missing
p1_h_acceptornumeric4 unique values
0 missing
p1_pi_donernumeric3 unique values
0 missing
p1_pi_acceptornumeric3 unique values
0 missing
p1_polarisablenumeric3 unique values
0 missing
p1_sigmanumeric4 unique values
0 missing
p1_branchnumeric3 unique values
0 missing
p2_polarnumeric6 unique values
0 missing
p2_sizenumeric4 unique values
0 missing
p2_flexnumeric2 unique values
0 missing
p2_h_donernumeric2 unique values
0 missing
p2_h_acceptornumeric2 unique values
0 missing
p2_pi_donernumeric2 unique values
0 missing
p2_pi_acceptornumeric3 unique values
0 missing
p2_polarisablenumeric3 unique values
0 missing
p2_sigmanumeric4 unique values
0 missing
p2_branchnumeric3 unique values
0 missing
p3_polarnumeric5 unique values
0 missing
p3_sizenumeric4 unique values
0 missing
p3_flexnumeric2 unique values
0 missing
p3_h_donernumeric2 unique values
0 missing
p3_h_acceptornumeric2 unique values
0 missing
p3_pi_donernumeric2 unique values
0 missing
p3_pi_acceptornumeric2 unique values
0 missing
p3_polarisablenumeric3 unique values
0 missing
p3_sigmanumeric4 unique values
0 missing
p3_branchnumeric3 unique values
0 missing
p4_polarnumeric5 unique values
0 missing
p4_sizenumeric9 unique values
0 missing
p4_flexnumeric8 unique values
0 missing
p4_h_donernumeric3 unique values
0 missing
p4_h_acceptornumeric4 unique values
0 missing
p4_pi_donernumeric3 unique values
0 missing
p4_pi_acceptornumeric3 unique values
0 missing
p4_polarisablenumeric3 unique values
0 missing
p4_sigmanumeric4 unique values
0 missing
p4_branchnumeric5 unique values
0 missing
p5_polarnumeric5 unique values
0 missing
p5_sizenumeric6 unique values
0 missing
p5_flexnumeric1 unique values
0 missing
p5_h_donernumeric1 unique values
0 missing
p5_h_acceptornumeric3 unique values
0 missing
p5_pi_donernumeric2 unique values
0 missing
p5_pi_acceptornumeric3 unique values
0 missing
p5_polarisablenumeric3 unique values
0 missing
p5_sigmanumeric4 unique values
0 missing
p5_branchnumeric2 unique values
0 missing
p6_polarnumeric5 unique values
0 missing
p6_sizenumeric5 unique values
0 missing
p6_flexnumeric2 unique values
0 missing
p6_h_donernumeric2 unique values
0 missing
p6_h_acceptornumeric2 unique values
0 missing
p6_pi_donernumeric2 unique values
0 missing
p6_pi_acceptornumeric2 unique values
0 missing
p6_polarisablenumeric3 unique values
0 missing
p6_sigmanumeric4 unique values
0 missing
p6_branchnumeric4 unique values
0 missing

19 properties

186
Number of instances (rows) of the dataset.
61
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.
61
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.83
Average class difference between consecutive instances.
0
Percentage of missing values.
0.33
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

5 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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