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topo_2_1

topo_2_1

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Author: Source: Unknown - Date unknown Please cite: This is one of 41 drug design datasets. The datasets with 1143 features are formed using Adriana.Code software (www.molecular-networks.com/software/adrianacode). The molecules and outputs are taken from the original studies (see below). The other datasets are taken exactly from the original studies. The last attribute in each file is the target. Original studies: carbolenes "B. D. Silverman and Daniel. E. Platt, J. Med. Chem. 1996, 39, 2129-2140" mtp2 "Bergstrom, C. A. S.; Norinder, U.; Luthman, K.; Artursson, P. Molecular Descriptors Influencing Melting Point and Their Role in Classification of Solid Drugs. J. Chem. Inf. Comput. Sci.; (Article); 2003; 43(4); 1177-1185" chang, cristalli, depreux, doherty, garrat2, garrat, heyl, krystek, lewis, penning, rosowsky, siddiqi, stevenson, strupcz, svensson, thompson, tsutumi, uejling, yokoyama1, yokoyama2 "David E Patterson, Richard D Cramer, Allan M Ferguson, Robert D Clark, Laurence W Weinberger. Neighbourhood Behaviour: A Useful Concept for Validation of ""Molecular Diversity"" Descriptors. J. Med. Chem. 1996 (39) 3049 - 3059." mtp "Karthikeyan, M.; Glen, R.C.; Bender, A. General melting point prediction based on a diverse compound dataset and artificial neural networks. J. Chem. Inf. Model.; 2005; 45(3); 581-590" benzo32 "Harrison,P.W. and Barlin,G.B. and Davies,L.P. and Ireland,S.J. and Matyus,P. and Wong,M.G., Syntheses, pharmacological evaluation and molecular modelling of substituted 6-alkoxyimidazo[1,2-b]pyridazines as new ligands for the benzodiazepine receptor, European Journal of Medicinal Chemistry, (31), 1996, 651-662" PHENETYL1 "H. Kubinyi (Ed.): ""QSAR: Hansch Analysis and Related Approaches"", VCH, Weinhein (Ger), 1993, pp.57-68" pah "Todeschini, R.; Gramatica, P.; Marengo, E.; Provenzani, R. Weighted Holistic Invariant Molecular Descriptors. Part 2. Theory Development and Applications on Modeling Physico-Chemical Properties of PolyAromatic Hydrocarbons (PAH). Chemom. Intell. Lab. Syst. 1995, 27, 221-229." pdgfr "R. Guha and P. Jurs. The Development of Linear, Ensemble and Non-linear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. J. Chem. Inf. Comput. Sci. 2004, 44 (6), 2179-2189" Phen "Cammarata, A. Interrelationship of the Regression Models Used for Structure-Activity Analyses. J. Med. Chem. 1972, 15, 573-577" topo_2_1, yprop_4_1 "Jun Feng et al, Predictive Toxicology: Benchmarking Molecular Descriptors and Statistical Methods, J. Chem. Inf Comput. Sci., 2003 (43) 1463-1470" qsabr1, qsabr2 "Damborsky, J., Schultz, T.W., Comparison of the QSAR models for toxicity and biodegradability of anilines and phenols, Chemosphere 34: 429-446, 1997" qsartox "Blaha, L., Damborsky, J., Nemec, M., QSAR for acute toxicity of saturated and unsaturated halogenated aliphatic compounds, Chemosphere 36: 1345-1365, 1998" qsbr_rw1 "Damborsky, J. et al., Structure-biodegradability relationships for chlorinated dibenzo-p-dioxins and dibenzofurans, In: Wittich, R.-M., Biodegradation of dioxins and furans, R.G. Landes Company, Austin, 1998" qsbr_y2 "Damborsky, J. et al., A mechanistic approach to deriving QSBR- A case study: dehalogenation of haloaliphatic compounds, In: Peijnenburg, W.J.G.M., Damborsky, J., Biodegradability Prediction, Kluwer Academic Publishers" qsbralks "Damborsky, J. et al., Mechanism-based Quantitative Structure-Biodegradability Relationships for hydrolytic dehalogenation of chloro- and bromo-alkenes, Quantitative Structure-Activity Relationships 17: 450-458, 1998" qsfrdhla "Damborsky, J., Quantitative structure-function relationships of the single-point mutants of haloalkane dehalogenase: A multivariate approach, Qunatitative Structure-Activity Relationships 16: 126-135, 1997" qsfsr1 "Damborsky, J., Quantitative structure-function and structure-stability relationships of purposely modified proteins, Protein Engineering 11: 21-30, 1998" qsfsr2 "Damborsky, J., Quantitative structure-function and structure-stability relationships of purposely modified proteins, Protein Engineering 11: 21-30, 1998" qsprcmpx "Cajan, M. et al., Stability of Aromatic Amides with Bromide Anion: Quantitative Structure-Property Relationships, Journal of Chemical Information and Computer Sciences, in press, 2000" selwood "Selwood, D. L.; Livingstone, D. J.; Comley, J. C.; O'Dowd, A. B.; Hudson, A. T.; Jackson, P.; Jandu, K. S.; Rose, V. S.; Stables, J. N. Structure-Activity Relationships of Antifilarial Antimycin Analogues: A Multivariate Pattern Recognition Study J. Med. Chem., 1990, 33, 136-142"

267 features

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0 missing
oz1numeric134 unique values
0 missing
oz257numeric47 unique values
0 missing
oz2numeric5102 unique values
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oz258numeric40 unique values
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oz3numeric1110 unique values
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oz266numeric6 unique values
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oz93numeric199 unique values
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oz94numeric246 unique values
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oz95numeric207 unique values
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oz97numeric46 unique values
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oz98numeric1288 unique values
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oz99numeric771 unique values
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oz100numeric2395 unique values
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oz101numeric1654 unique values
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oz102numeric5495 unique values
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oz103numeric373 unique values
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oz104numeric2181 unique values
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oz105numeric376 unique values
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oz106numeric254 unique values
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oz107numeric1655 unique values
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oz108numeric1430 unique values
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oz109numeric2053 unique values
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oz110numeric3103 unique values
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oz111numeric1193 unique values
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oz112numeric1022 unique values
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oz113numeric7339 unique values
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oz114numeric6978 unique values
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oz115numeric996 unique values
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oz116numeric2707 unique values
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oz117numeric1883 unique values
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oz118numeric602 unique values
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oz119numeric6990 unique values
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oz120numeric767 unique values
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oz121numeric957 unique values
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oz122numeric1789 unique values
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oz123numeric1110 unique values
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oz124numeric5101 unique values
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oz125numeric414 unique values
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oz126numeric2458 unique values
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oz127numeric382 unique values
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oz128numeric2008 unique values
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oz129numeric7278 unique values
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oz130numeric625 unique values
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oz131numeric2806 unique values
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oz132numeric584 unique values
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oz133numeric2172 unique values
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oz134numeric6358 unique values
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oz135numeric583 unique values
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oz136numeric2296 unique values
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oz137numeric558 unique values
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oz138numeric2111 unique values
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oz139numeric4679 unique values
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oz140numeric519 unique values
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oz141numeric1913 unique values
0 missing
oz142numeric483 unique values
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oz143numeric2078 unique values
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oz144numeric4150 unique values
0 missing
oz145numeric478 unique values
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oz146numeric1719 unique values
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oz147numeric462 unique values
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oz148numeric2094 unique values
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oz149numeric4061 unique values
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oz150numeric478 unique values
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oz151numeric1663 unique values
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oz152numeric458 unique values
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oz153numeric753 unique values
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oz154numeric536 unique values
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oz155numeric8528 unique values
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oz156numeric8545 unique values
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oz157numeric8519 unique values
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oz158numeric8570 unique values
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oz159numeric8512 unique values
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oz160numeric931 unique values
0 missing
oz161numeric1068 unique values
0 missing
oz162numeric1198 unique values
0 missing
oz163numeric1063 unique values
0 missing
oz164numeric1240 unique values
0 missing
oz165numeric1 unique values
0 missing
oz166numeric1 unique values
0 missing
oz167numeric1 unique values
0 missing
oz168numeric1 unique values
0 missing
oz169numeric1 unique values
0 missing
oz170numeric2264 unique values
0 missing
oz171numeric294 unique values
0 missing
oz172numeric108 unique values
0 missing
oz173numeric136 unique values
0 missing
oz174numeric1623 unique values
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oz175numeric187 unique values
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oz176numeric7604 unique values
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oz177numeric3526 unique values
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oz178numeric2311 unique values
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oz179numeric240 unique values
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oz180numeric8647 unique values
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oz181numeric3840 unique values
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oz182numeric2322 unique values
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oz183numeric242 unique values
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oz184numeric8633 unique values
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oz185numeric3859 unique values
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oz186numeric2862 unique values
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oz187numeric244 unique values
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oz188numeric8662 unique values
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oz190numeric2131 unique values
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oz191numeric218 unique values
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oz192numeric8657 unique values
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oz193numeric3853 unique values
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oz194numeric2927 unique values
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oz195numeric250 unique values
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oz196numeric8652 unique values
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oz197numeric3880 unique values
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oz198numeric116 unique values
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oz199numeric150 unique values
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oz200numeric213 unique values
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oz201numeric291 unique values
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oz202numeric345 unique values
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oz213numeric5337 unique values
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oz214numeric4917 unique values
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oz215numeric2696 unique values
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oz216numeric303 unique values
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oz217numeric1296 unique values
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oz218numeric307 unique values
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oz219numeric6959 unique values
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oz221numeric182 unique values
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oz222numeric103 unique values
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oz223numeric2768 unique values
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oz224numeric6123 unique values
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oz225numeric316 unique values
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oz226numeric351 unique values
0 missing
oz227numeric1509 unique values
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oz228numeric1769 unique values
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oz229numeric235 unique values
0 missing
oz230numeric234 unique values
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oz231numeric191 unique values
0 missing
oz232numeric325 unique values
0 missing
oz233numeric96 unique values
0 missing
oz234numeric21 unique values
0 missing
oz235numeric86 unique values
0 missing
oz236numeric104 unique values
0 missing
oz237numeric48 unique values
0 missing
oz238numeric19 unique values
0 missing
oz239numeric367 unique values
0 missing
oz240numeric127 unique values
0 missing
oz241numeric36 unique values
0 missing
oz242numeric102 unique values
0 missing
oz243numeric127 unique values
0 missing
oz244numeric70 unique values
0 missing
oz245numeric31 unique values
0 missing
oz246numeric40 unique values
0 missing
oz247numeric8 unique values
0 missing
oz248numeric34 unique values
0 missing
oz249numeric46 unique values
0 missing
oz250numeric20 unique values
0 missing
oz251numeric8 unique values
0 missing
oz252numeric10 unique values
0 missing
oz253numeric7 unique values
0 missing
oz254numeric15 unique values
0 missing
oz255numeric6 unique values
0 missing
oz256numeric2 unique values
0 missing

107 properties

8885
Number of instances (rows) of the dataset.
267
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.
267
Number of numeric attributes.
0
Number 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.
Number of instances belonging to the least frequent class.
0
Percentage of nominal attributes.
7.28
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
480.99
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.11
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
0.2
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.01
First quartile of kurtosis 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
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.03
First quartile of means among attributes of the numeric type.
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
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.
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
Average number of distinct values among the attributes of the nominal type.
0.87
First quartile of skewness among 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
9.32
Mean skewness among attributes of the numeric type.
0.05
First quartile of standard deviation of attributes of the numeric type.
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.
0.08
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
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.
Minimal entropy among attributes.
8.4
Second quartile (Median) of kurtosis 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.
-0.2
Minimum kurtosis among attributes of the numeric type.
0.16
Second quartile (Median) of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
8885
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.92
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.9
Second quartile (Median) of skewness among attributes of the numeric type.
0.08
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
0.03
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
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.
-2.23
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
84.17
Third quartile of kurtosis among attributes of the numeric type.
0.97
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
94.26
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.27
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
0.17
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric attributes.

5 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz267
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: oz267
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task