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triazines

triazines

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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
p4_sizenumeric9 unique values
0 missing
p4_polarnumeric5 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
p2_pi_donernumeric2 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
p1_polarnumeric6 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
Show all 61 features

107 properties

NumberOfInstances
186
Number of instances (rows) of the dataset.
NumberOfFeatures
61
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
61
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
AutoCorrelation
0.83
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.33
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
186
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.65
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
13.64
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.38
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
18.41
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.21
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.19
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.18
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.57
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.35
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.21
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.12
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.75
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.13
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
5.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.18
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
2.57
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.17
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
26.78
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.29
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
5.34
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.23
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Regression on triazines
5 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: activity
Supervised Regression on triazines
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: activity
Clustering on triazines
0 runs
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
Clustering on triazines
0 runs - estimation_procedure: 50 times Clustering
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