Data
micro-mass

micro-mass

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael Gomes Mantovani
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Author: Pierre Mahé, Jean-Baptiste Veyrieras Source: [original](http://www.openml.org/d/1514) - UCI Please cite: * Dataset Title: MicroMass - Pure (pure spectra version) * Abstract: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data. * Source: Pierre Mahé, pierre.mahe '@' biomerieux.com, bioMérieux Jean-Baptiste Veyrieras, jean-baptiste.veyrieras '@' biomerieux.com, bioMérieux * Data Set Information: This MALDI-TOF dataset consists in: A) A reference panel of 20 Gram positive and negative bacterial species covering 9 genera among which several species are known to be hard to discriminate by mass spectrometry (MALDI-TOF). Each species was represented by 11 to 60 mass spectra obtained from 7 to 20 bacterial strains, constituting altogether a dataset of 571 spectra obtained from 213 strains. The spectra were obtained according to the standard culture-based workflow used in clinical routine in which the microorganism was first grown on an agar plate for 24 to 48 hours, before a portion of colony was picked, spotted on a MALDI slide and a mass spectrum was acquired. B) Based on this reference panel, a dedicated in vitro mock-up mixture dataset was constituted. For that purpose we considered 10 pairs of species of various taxonomic proximity: * 4 mixtures, labelled A, B, C and D, involved species that belong to the same genus, * 2 mixtures, labelled E and F, involved species that belong to distinct genera, but to the same Gram type, * 4 mixtures, labelled G, H, I and J, involved species that belong to distinct Gram types. Each mixture was represented by 2 pairs of strains, which were mixed according to the following 9 concentration ratios : 1:0, 10:1, 5:1, 2:1, 1:1, 1:2, 1:5, 1:10, 0:1. Two replicate spectra were acquired for each concentration ratio and each couple of strains, leading altogether to a dataset of 360 spectra, among which 80 are actually pure sample spectra. * Relevant Papers: Mahé et al. (2014). Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum. Bioinformatics. Vervier et al., A benchmark of support vector machines strategies for microbial identification by mass-spectrometry data, submitted

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V630numeric15 unique values
0 missing
V631numeric70 unique values
0 missing
V632numeric95 unique values
0 missing
V633numeric33 unique values
0 missing
V634numeric1 unique values
0 missing
V635numeric54 unique values
0 missing
V636numeric84 unique values
0 missing
V637numeric2 unique values
0 missing
V638numeric1 unique values
0 missing
V639numeric77 unique values
0 missing
V640numeric1 unique values
0 missing
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0 missing
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0 missing
V643numeric58 unique values
0 missing
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0 missing
V645numeric75 unique values
0 missing
V646numeric87 unique values
0 missing
V647numeric3 unique values
0 missing
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0 missing
V649numeric153 unique values
0 missing
V650numeric149 unique values
0 missing
V651numeric135 unique values
0 missing
V652numeric84 unique values
0 missing
V653numeric6 unique values
0 missing
V654numeric1 unique values
0 missing
V655numeric30 unique values
0 missing
V656numeric58 unique values
0 missing
V657numeric47 unique values
0 missing
V658numeric112 unique values
0 missing
V659numeric82 unique values
0 missing
V660numeric10 unique values
0 missing
V661numeric23 unique values
0 missing
V662numeric28 unique values
0 missing
V663numeric4 unique values
0 missing
V664numeric1 unique values
0 missing
V665numeric91 unique values
0 missing
V666numeric51 unique values
0 missing
V667numeric15 unique values
0 missing
V668numeric1 unique values
0 missing
V669numeric9 unique values
0 missing
V670numeric61 unique values
0 missing
V671numeric72 unique values
0 missing
V672numeric67 unique values
0 missing
V673numeric169 unique values
0 missing
V674numeric54 unique values
0 missing
V675numeric67 unique values
0 missing
V676numeric1 unique values
0 missing
V677numeric6 unique values
0 missing
V678numeric55 unique values
0 missing
V679numeric50 unique values
0 missing
V680numeric1 unique values
0 missing
V681numeric64 unique values
0 missing
V682numeric85 unique values
0 missing
V683numeric1 unique values
0 missing
V684numeric92 unique values
0 missing
V685numeric128 unique values
0 missing
V686numeric105 unique values
0 missing
V687numeric1 unique values
0 missing
V688numeric53 unique values
0 missing
V689numeric106 unique values
0 missing
V690numeric80 unique values
0 missing
V691numeric1 unique values
0 missing
V692numeric30 unique values
0 missing
V693numeric107 unique values
0 missing
V694numeric132 unique values
0 missing
V695numeric213 unique values
0 missing
V696numeric1 unique values
0 missing
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0 missing
V698numeric63 unique values
0 missing
V699numeric30 unique values
0 missing
V700numeric1 unique values
0 missing
V701numeric88 unique values
0 missing
V702numeric1 unique values
0 missing
V703numeric26 unique values
0 missing
V704numeric81 unique values
0 missing
V705numeric70 unique values
0 missing
V706numeric44 unique values
0 missing
V707numeric23 unique values
0 missing
V708numeric2 unique values
0 missing
V709numeric13 unique values
0 missing
V710numeric8 unique values
0 missing
V711numeric134 unique values
0 missing
V712numeric89 unique values
0 missing
V713numeric86 unique values
0 missing
V714numeric3 unique values
0 missing
V715numeric86 unique values
0 missing
V716numeric84 unique values
0 missing
V717numeric15 unique values
0 missing
V718numeric1 unique values
0 missing
V719numeric294 unique values
0 missing
V720numeric83 unique values
0 missing
V721numeric1 unique values
0 missing
V722numeric34 unique values
0 missing
V723numeric29 unique values
0 missing
V724numeric6 unique values
0 missing
V725numeric1 unique values
0 missing
V726numeric10 unique values
0 missing
V727numeric10 unique values
0 missing
V728numeric1 unique values
0 missing
V729numeric204 unique values
0 missing
V730numeric1 unique values
0 missing
V731numeric36 unique values
0 missing
V732numeric18 unique values
0 missing
V733numeric48 unique values
0 missing
V734numeric13 unique values
0 missing
V735numeric49 unique values
0 missing
V736numeric14 unique values
0 missing
V737numeric1 unique values
0 missing
V738numeric62 unique values
0 missing
V739numeric38 unique values
0 missing
V740numeric42 unique values
0 missing
V741numeric64 unique values
0 missing
V742numeric30 unique values
0 missing
V743numeric12 unique values
0 missing
V744numeric205 unique values
0 missing
V745numeric44 unique values
0 missing
V746numeric26 unique values
0 missing
V747numeric92 unique values
0 missing
V748numeric5 unique values
0 missing
V749numeric2 unique values
0 missing
V750numeric88 unique values
0 missing
V751numeric112 unique values
0 missing
V752numeric63 unique values
0 missing
V753numeric57 unique values
0 missing
V754numeric40 unique values
0 missing
V755numeric84 unique values
0 missing
V756numeric4 unique values
0 missing
V757numeric53 unique values
0 missing
V758numeric65 unique values
0 missing
V759numeric53 unique values
0 missing
V760numeric1 unique values
0 missing
V761numeric195 unique values
0 missing
V762numeric1 unique values
0 missing
V763numeric117 unique values
0 missing
V764numeric1 unique values
0 missing
V765numeric122 unique values
0 missing
V766numeric1 unique values
0 missing
V767numeric44 unique values
0 missing
V768numeric9 unique values
0 missing
V769numeric4 unique values
0 missing
V770numeric1 unique values
0 missing
V771numeric3 unique values
0 missing
V772numeric169 unique values
0 missing
V773numeric50 unique values
0 missing
V774numeric73 unique values
0 missing
V775numeric5 unique values
0 missing
V776numeric45 unique values
0 missing
V777numeric43 unique values
0 missing
V778numeric80 unique values
0 missing
V779numeric5 unique values
0 missing
V780numeric5 unique values
0 missing
V781numeric100 unique values
0 missing
V782numeric1 unique values
0 missing
V783numeric50 unique values
0 missing
V784numeric87 unique values
0 missing
V785numeric84 unique values
0 missing
V786numeric1 unique values
0 missing
V787numeric136 unique values
0 missing
V788numeric12 unique values
0 missing
V789numeric94 unique values
0 missing
V790numeric7 unique values
0 missing
V791numeric27 unique values
0 missing
V792numeric18 unique values
0 missing
V793numeric27 unique values
0 missing
V794numeric21 unique values
0 missing
V795numeric26 unique values
0 missing
V796numeric76 unique values
0 missing
V797numeric61 unique values
0 missing
V798numeric1 unique values
0 missing
V799numeric36 unique values
0 missing
V800numeric40 unique values
0 missing
V801numeric1 unique values
0 missing
V802numeric53 unique values
0 missing
V803numeric13 unique values
0 missing
V804numeric53 unique values
0 missing
V805numeric79 unique values
0 missing
V806numeric15 unique values
0 missing
V807numeric58 unique values
0 missing
V808numeric41 unique values
0 missing
V809numeric11 unique values
0 missing
V810numeric92 unique values
0 missing
V811numeric126 unique values
0 missing
V812numeric41 unique values
0 missing
V813numeric1 unique values
0 missing
V814numeric64 unique values
0 missing
V815numeric3 unique values
0 missing
V816numeric1 unique values
0 missing
V817numeric72 unique values
0 missing
V818numeric100 unique values
0 missing
V819numeric131 unique values
0 missing
V820numeric60 unique values
0 missing
V821numeric13 unique values
0 missing
V822numeric44 unique values
0 missing
V823numeric47 unique values
0 missing
V824numeric2 unique values
0 missing
V825numeric56 unique values
0 missing
V826numeric49 unique values
0 missing
V827numeric21 unique values
0 missing
V828numeric8 unique values
0 missing
V829numeric125 unique values
0 missing
V830numeric73 unique values
0 missing
V831numeric13 unique values
0 missing
V832numeric43 unique values
0 missing
V833numeric76 unique values
0 missing
V834numeric1 unique values
0 missing
V835numeric17 unique values
0 missing
V836numeric4 unique values
0 missing
V837numeric96 unique values
0 missing
V838numeric296 unique values
0 missing
V839numeric3 unique values
0 missing
V840numeric1 unique values
0 missing
V841numeric10 unique values
0 missing
V842numeric8 unique values
0 missing
V843numeric79 unique values
0 missing
V844numeric2 unique values
0 missing
V845numeric1 unique values
0 missing
V846numeric41 unique values
0 missing
V847numeric9 unique values
0 missing
V848numeric72 unique values
0 missing
V849numeric54 unique values
0 missing
V850numeric1 unique values
0 missing
V851numeric1 unique values
0 missing
V852numeric108 unique values
0 missing
V853numeric119 unique values
0 missing
V854numeric1 unique values
0 missing
V855numeric49 unique values
0 missing
V856numeric6 unique values
0 missing
V857numeric1 unique values
0 missing
V858numeric3 unique values
0 missing
V859numeric18 unique values
0 missing
V860numeric13 unique values
0 missing
V861numeric38 unique values
0 missing
V862numeric49 unique values
0 missing
V863numeric23 unique values
0 missing
V864numeric81 unique values
0 missing
V865numeric81 unique values
0 missing
V866numeric1 unique values
0 missing
V867numeric9 unique values
0 missing
V868numeric50 unique values
0 missing
V869numeric6 unique values
0 missing
V870numeric2 unique values
0 missing
V871numeric84 unique values
0 missing
V872numeric1 unique values
0 missing
V873numeric6 unique values
0 missing
V874numeric11 unique values
0 missing
V875numeric54 unique values
0 missing
V876numeric19 unique values
0 missing
V877numeric108 unique values
0 missing
V878numeric47 unique values
0 missing
V879numeric4 unique values
0 missing
V880numeric1 unique values
0 missing
V881numeric4 unique values
0 missing
V882numeric1 unique values
0 missing
V883numeric6 unique values
0 missing
V884numeric5 unique values
0 missing
V885numeric35 unique values
0 missing
V886numeric3 unique values
0 missing
V887numeric37 unique values
0 missing
V888numeric79 unique values
0 missing
V889numeric89 unique values
0 missing
V890numeric97 unique values
0 missing
V891numeric48 unique values
0 missing
V892numeric31 unique values
0 missing
V893numeric58 unique values
0 missing
V894numeric65 unique values
0 missing
V895numeric1 unique values
0 missing
V896numeric54 unique values
0 missing
V897numeric69 unique values
0 missing
V898numeric1 unique values
0 missing
V899numeric1 unique values
0 missing
V900numeric1 unique values
0 missing
V901numeric45 unique values
0 missing
V902numeric2 unique values
0 missing
V903numeric30 unique values
0 missing
V904numeric17 unique values
0 missing
V905numeric40 unique values
0 missing
V906numeric66 unique values
0 missing
V907numeric1 unique values
0 missing
V908numeric2 unique values
0 missing
V909numeric56 unique values
0 missing
V910numeric88 unique values
0 missing
V911numeric90 unique values
0 missing
V912numeric20 unique values
0 missing
V913numeric22 unique values
0 missing
V914numeric14 unique values
0 missing
V915numeric1 unique values
0 missing
V916numeric78 unique values
0 missing
V917numeric70 unique values
0 missing
V918numeric124 unique values
0 missing
V919numeric82 unique values
0 missing
V920numeric110 unique values
0 missing
V921numeric22 unique values
0 missing
V922numeric19 unique values
0 missing
V923numeric57 unique values
0 missing
V924numeric1 unique values
0 missing
V925numeric68 unique values
0 missing
V926numeric1 unique values
0 missing
V927numeric1 unique values
0 missing
V928numeric1 unique values
0 missing
V929numeric94 unique values
0 missing
V930numeric1 unique values
0 missing
V931numeric1 unique values
0 missing
V932numeric38 unique values
0 missing
V933numeric4 unique values
0 missing
V934numeric1 unique values
0 missing
V935numeric109 unique values
0 missing
V936numeric84 unique values
0 missing
V937numeric160 unique values
0 missing
V938numeric77 unique values
0 missing
V939numeric61 unique values
0 missing
V940numeric41 unique values
0 missing
V941numeric26 unique values
0 missing
V942numeric51 unique values
0 missing
V943numeric147 unique values
0 missing
V944numeric1 unique values
0 missing
V945numeric71 unique values
0 missing
V946numeric1 unique values
0 missing
V947numeric62 unique values
0 missing
V948numeric7 unique values
0 missing
V949numeric22 unique values
0 missing
V950numeric30 unique values
0 missing
V951numeric38 unique values
0 missing
V952numeric4 unique values
0 missing
V953numeric80 unique values
0 missing
V954numeric104 unique values
0 missing
V955numeric123 unique values
0 missing
V956numeric1 unique values
0 missing
V957numeric73 unique values
0 missing
V958numeric84 unique values
0 missing
V959numeric86 unique values
0 missing
V960numeric92 unique values
0 missing
V961numeric20 unique values
0 missing
V962numeric1 unique values
0 missing
V963numeric7 unique values
0 missing
V964numeric109 unique values
0 missing
V965numeric48 unique values
0 missing
V966numeric48 unique values
0 missing
V967numeric11 unique values
0 missing
V968numeric88 unique values
0 missing
V969numeric10 unique values
0 missing
V970numeric21 unique values
0 missing
V971numeric71 unique values
0 missing
V972numeric44 unique values
0 missing
V973numeric2 unique values
0 missing
V974numeric42 unique values
0 missing
V975numeric57 unique values
0 missing
V976numeric123 unique values
0 missing
V977numeric111 unique values
0 missing
V978numeric81 unique values
0 missing
V979numeric52 unique values
0 missing
V980numeric51 unique values
0 missing
V981numeric4 unique values
0 missing
V982numeric4 unique values
0 missing
V983numeric2 unique values
0 missing
V984numeric62 unique values
0 missing
V985numeric26 unique values
0 missing
V986numeric40 unique values
0 missing
V987numeric69 unique values
0 missing
V988numeric45 unique values
0 missing
V989numeric5 unique values
0 missing
V990numeric9 unique values
0 missing
V991numeric22 unique values
0 missing
V992numeric78 unique values
0 missing
V993numeric29 unique values
0 missing
V994numeric64 unique values
0 missing
V995numeric30 unique values
0 missing
V996numeric50 unique values
0 missing
V997numeric3 unique values
0 missing
V998numeric17 unique values
0 missing
V999numeric45 unique values
0 missing

107 properties

571
Number of instances (rows) of the dataset.
1301
Number of attributes (columns) of the dataset.
20
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.
1300
Number of numeric attributes.
1
Number of nominal attributes.
0.7
Average class difference between consecutive instances.
0.88
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
0.28
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
0.7
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
0.88
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
0.28
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
0.7
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
0.88
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
0.28
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
0.7
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
4.2
Entropy of the target attribute values.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.82
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
2.28
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.69
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
10.51
Percentage of instances belonging to the most frequent class.
60
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
571
Maximum kurtosis among attributes of the numeric type.
2882892.51
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
20
The maximum number of distinct values among attributes of the nominal type.
23.9
Maximum skewness among attributes of the numeric type.
7792765.65
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
212.99
Mean kurtosis among attributes of the numeric type.
67437.29
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
20
Average number of distinct values among the attributes of the nominal type.
12.82
Mean skewness among attributes of the numeric type.
343176.13
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
8.92
Minimum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
20
The minimal number of distinct values among attributes of the nominal type.
2.8
Minimum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
1.93
Percentage of instances belonging to the least frequent class.
11
Number of instances belonging to the least frequent class.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
99.92
Percentage of numeric attributes.
0.08
Percentage of nominal attributes.
First quartile of entropy among attributes.
83.85
First quartile of kurtosis among attributes of the numeric type.
681.2
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
8.34
First quartile of skewness among attributes of the numeric type.
9417.27
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
157.41
Second quartile (Median) of kurtosis among attributes of the numeric type.
13117.02
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
11.52
Second quartile (Median) of skewness among attributes of the numeric type.
94709.23
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
306.34
Third quartile of kurtosis among attributes of the numeric type.
50568.45
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
16.91
Third quartile of skewness among attributes of the numeric type.
357114.78
Third quartile of standard deviation of attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.54
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.54
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.54
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.4
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Define a new task