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

107 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.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
Average number of distinct values among the attributes of the nominal type.
0.75
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.19
Mean skewness among attributes of the numeric type.
0.13
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.18
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
5.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
-1.57
Minimum kurtosis among attributes of the numeric type.
0.18
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
186
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2.57
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.65
Maximum of means among attributes of the numeric type.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.17
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.33
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-1.35
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
26.78
Third quartile of kurtosis among attributes of the numeric type.
0.83
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
13.64
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric attributes.
0.29
Third quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.38
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
5.34
Third quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
18.41
Mean kurtosis among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.21
First quartile of kurtosis among attributes of the numeric type.
0.23
Third quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.21
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.12
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

5 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
Define a new task