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oh5.wc

active Sparse_ARFF Publicly available Visibility: public Uploaded 28-09-2014 by Joaquin Vanschoren
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Author: [George Forman](https://scholar.google.com/citations?user=r0a222QAAAAJ) Source: http://tunedit.org/repo/Data/Text-wc/oh5.wc.arff Please cite:

3013 features

CLASS_LABEL (target)	nominal	10 unique values 0 missing
depart	numeric	3 unique values 0 missing
resid	numeric	2 unique values 0 missing
encount	numeric	3 unique values 0 missing
inabl	numeric	2 unique values 0 missing
presum	numeric	2 unique values 0 missing
deficit	numeric	4 unique values 0 missing
esophagu	numeric	4 unique values 0 missing
colon	numeric	6 unique values 0 missing
amplitud	numeric	4 unique values 0 missing
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nephropathi	numeric	4 unique values 0 missing
literatur	numeric	2 unique values 0 missing
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conclud	numeric	3 unique values 0 missing
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interpeak	numeric	3 unique values 0 missing
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cellul	numeric	3 unique values 0 missing
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commun	numeric	3 unique values 0 missing
min	numeric	7 unique values 0 missing
cluster	numeric	3 unique values 0 missing
tool	numeric	2 unique values 0 missing
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impact	numeric	3 unique values 0 missing
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epinephrin	numeric	5 unique values 0 missing
aqueou	numeric	4 unique values 0 missing
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t	numeric	2 unique values 0 missing
scor	numeric	2 unique values 0 missing
urinari	numeric	6 unique values 0 missing
girl	numeric	3 unique values 0 missing
cause	numeric	4 unique values 0 missing
center	numeric	3 unique values 0 missing
nmol	numeric	4 unique values 0 missing
ursodeoxychol	numeric	6 unique values 0 missing
mannitol	numeric	5 unique values 0 missing
tinnitu	numeric	4 unique values 0 missing
perform	numeric	5 unique values 0 missing
accordingli	numeric	2 unique values 0 missing
v	numeric	8 unique values 0 missing
closur	numeric	2 unique values 0 missing
isotop	numeric	2 unique values 0 missing
purifi	numeric	5 unique values 0 missing
observ	numeric	5 unique values 0 missing
depletion	numeric	3 unique values 0 missing
coeffici	numeric	3 unique values 0 missing
desatur	numeric	3 unique values 0 missing
fundament	numeric	2 unique values 0 missing
serum	numeric	8 unique values 0 missing
cover	numeric	3 unique values 0 missing
american	numeric	3 unique values 0 missing
ammonia	numeric	4 unique values 0 missing
fresh	numeric	4 unique values 0 missing
enabl	numeric	2 unique values 0 missing
adenosin	numeric	4 unique values 0 missing
refin	numeric	2 unique values 0 missing
profil	numeric	5 unique values 0 missing
incompat	numeric	4 unique values 0 missing
qualiti	numeric	5 unique values 0 missing
reach	numeric	2 unique values 0 missing
pulsatil	numeric	3 unique values 0 missing
routin	numeric	4 unique values 0 missing
nerve	numeric	7 unique values 0 missing
metabol	numeric	9 unique values 0 missing
mental	numeric	4 unique values 0 missing
induct	numeric	4 unique values 0 missing
bjork	numeric	4 unique values 0 missing
promin	numeric	3 unique values 0 missing
fuel	numeric	3 unique values 0 missing
island	numeric	5 unique values 0 missing
hand	numeric	2 unique values 0 missing
indium	numeric	3 unique values 0 missing
analyz	numeric	2 unique values 0 missing
fourteen	numeric	2 unique values 0 missing
phosphatidylethanolamin	numeric	3 unique values 0 missing
manag	numeric	6 unique values 0 missing
aortic	numeric	5 unique values 0 missing
widespread	numeric	2 unique values 0 missing
flow	numeric	7 unique values 0 missing
psycholog	numeric	2 unique values 0 missing
hg	numeric	4 unique values 0 missing
exchang	numeric	4 unique values 0 missing
threaten	numeric	3 unique values 0 missing
food	numeric	4 unique values 0 missing
conduct	numeric	5 unique values 0 missing
pulse	numeric	3 unique values 0 missing
erythromycin	numeric	3 unique values 0 missing
vesicl	numeric	7 unique values 0 missing
faster	numeric	2 unique values 0 missing
abund	numeric	3 unique values 0 missing
pmol	numeric	5 unique values 0 missing
clonal	numeric	4 unique values 0 missing
rais	numeric	4 unique values 0 missing
consequ	numeric	3 unique values 0 missing
maximum	numeric	3 unique values 0 missing
react	numeric	3 unique values 0 missing
subject	numeric	8 unique values 0 missing
central	numeric	3 unique values 0 missing
size	numeric	7 unique values 0 missing
vice	numeric	2 unique values 0 missing
suspect	numeric	5 unique values 0 missing
predictor	numeric	5 unique values 0 missing
bare	numeric	2 unique values 0 missing
promis	numeric	2 unique values 0 missing
vacuum	numeric	2 unique values 0 missing
make	numeric	2 unique values 0 missing
anim	numeric	8 unique values 0 missing
valid	numeric	2 unique values 0 missing
intracerebr	numeric	3 unique values 0 missing
tim	numeric	3 unique values 0 missing
crucial	numeric	2 unique values 0 missing
omega	numeric	2 unique values 0 missing
expos	numeric	5 unique values 0 missing
exist	numeric	3 unique values 0 missing
hmg	numeric	4 unique values 0 missing
repetit	numeric	2 unique values 0 missing
respiratori	numeric	5 unique values 0 missing
underestim	numeric	3 unique values 0 missing
centrifug	numeric	4 unique values 0 missing
foot	numeric	3 unique values 0 missing
output	numeric	4 unique values 0 missing
bladder	numeric	5 unique values 0 missing
ablat	numeric	4 unique values 0 missing
subclin	numeric	3 unique values 0 missing
sixth	numeric	2 unique values 0 missing
antisera	numeric	4 unique values 0 missing
allograft	numeric	9 unique values 0 missing
endothelium	numeric	4 unique values 0 missing
bas	numeric	5 unique values 0 missing
suggest	numeric	4 unique values 0 missing
ipsilater	numeric	2 unique values 0 missing
shift	numeric	3 unique values 0 missing
suboptim	numeric	2 unique values 0 missing
preclud	numeric	2 unique values 0 missing
mol	numeric	3 unique values 0 missing
loud	numeric	3 unique values 0 missing
sixti	numeric	2 unique values 0 missing
undetect	numeric	3 unique values 0 missing
globulin	numeric	3 unique values 0 missing
autotransplant	numeric	3 unique values 0 missing
trap	numeric	2 unique values 0 missing
daili	numeric	4 unique values 0 missing
catheter	numeric	4 unique values 0 missing
ratio	numeric	7 unique values 0 missing
sum	numeric	2 unique values 0 missing
adequ	numeric	3 unique values 0 missing
usag	numeric	2 unique values 0 missing
consum	numeric	3 unique values 0 missing
predisposit	numeric	2 unique values 0 missing
unirradi	numeric	3 unique values 0 missing
sensorineur	numeric	5 unique values 0 missing
slightli	numeric	3 unique values 0 missing
activ	numeric	12 unique values 0 missing
thrombin	numeric	4 unique values 0 missing
chow	numeric	4 unique values 0 missing
reagent	numeric	5 unique values 0 missing
term	numeric	5 unique values 0 missing
amino	numeric	10 unique values 0 missing
mov	numeric	2 unique values 0 missing
feasibl	numeric	2 unique values 0 missing
gel	numeric	5 unique values 0 missing
leukotrien	numeric	2 unique values 0 missing
subcellular	numeric	5 unique values 0 missing
earlier	numeric	3 unique values 0 missing
aggrav	numeric	2 unique values 0 missing
transmiss	numeric	4 unique values 0 missing
course	numeric	5 unique values 0 missing
wastag	numeric	2 unique values 0 missing
resolv	numeric	3 unique values 0 missing
depress	numeric	4 unique values 0 missing
catabol	numeric	5 unique values 0 missing
outcom	numeric	5 unique values 0 missing
justifi	numeric	2 unique values 0 missing
hybrid	numeric	4 unique values 0 missing
cochlear	numeric	7 unique values 0 missing
episod	numeric	5 unique values 0 missing
hr	numeric	6 unique values 0 missing
pretreat	numeric	5 unique values 0 missing
instrum	numeric	2 unique values 0 missing
mumol	numeric	5 unique values 0 missing
ldl	numeric	6 unique values 0 missing
retest	numeric	3 unique values 0 missing
notion	numeric	2 unique values 0 missing
detect	numeric	5 unique values 0 missing
eleven	numeric	2 unique values 0 missing
migrat	numeric	3 unique values 0 missing
deethylas	numeric	3 unique values 0 missing
semi	numeric	2 unique values 0 missing
hz	numeric	7 unique values 0 missing
calmodulin	numeric	4 unique values 0 missing
nevertheless	numeric	2 unique values 0 missing
caus	numeric	5 unique values 0 missing
necessit	numeric	2 unique values 0 missing
skill	numeric	2 unique values 0 missing
ulcer	numeric	3 unique values 0 missing
irradi	numeric	8 unique values 0 missing
protein	numeric	15 unique values 0 missing
themselv	numeric	2 unique values 0 missing
novel	numeric	2 unique values 0 missing
extracrani	numeric	3 unique values 0 missing
knee	numeric	2 unique values 0 missing
zn	numeric	3 unique values 0 missing
biolog	numeric	2 unique values 0 missing
antithyroid	numeric	2 unique values 0 missing
consecut	numeric	3 unique values 0 missing
saphen	numeric	5 unique values 0 missing
platelet	numeric	9 unique values 0 missing
particip	numeric	3 unique values 0 missing
membran	numeric	12 unique values 0 missing
pilot	numeric	3 unique values 0 missing
advers	numeric	3 unique values 0 missing
phase	numeric	6 unique values 0 missing
mouse	numeric	3 unique values 0 missing
transition	numeric	4 unique values 0 missing
score	numeric	6 unique values 0 missing
toward	numeric	3 unique values 0 missing
serve	numeric	3 unique values 0 missing
intervent	numeric	6 unique values 0 missing
morbid	numeric	4 unique values 0 missing
treate	numeric	6 unique values 0 missing
cerebrovascular	numeric	3 unique values 0 missing
angiographi	numeric	4 unique values 0 missing
ethanol	numeric	10 unique values 0 missing
fetu	numeric	2 unique values 0 missing
adhesion	numeric	4 unique values 0 missing
guide	numeric	2 unique values 0 missing
cfu	numeric	5 unique values 0 missing
microscopi	numeric	3 unique values 0 missing
construct	numeric	2 unique values 0 missing
medium	numeric	5 unique values 0 missing
headach	numeric	4 unique values 0 missing
death	numeric	5 unique values 0 missing
reson	numeric	4 unique values 0 missing
statist	numeric	4 unique values 0 missing
tumour	numeric	3 unique values 0 missing
underw	numeric	4 unique values 0 missing
aminotransferas	numeric	3 unique values 0 missing
phenobarbit	numeric	3 unique values 0 missing
monkei	numeric	4 unique values 0 missing
myocardi	numeric	6 unique values 0 missing
quantit	numeric	4 unique values 0 missing
intub	numeric	4 unique values 0 missing
antiplatelet	numeric	5 unique values 0 missing
unsatisfactori	numeric	2 unique values 0 missing
latter	numeric	2 unique values 0 missing
lactogen	numeric	2 unique values 0 missing
branch	numeric	5 unique values 0 missing
wait	numeric	3 unique values 0 missing
ie	numeric	2 unique values 0 missing
therapeut	numeric	3 unique values 0 missing
autogen	numeric	4 unique values 0 missing
dramat	numeric	2 unique values 0 missing
instead	numeric	2 unique values 0 missing
cyst	numeric	4 unique values 0 missing
supplement	numeric	5 unique values 0 missing
undergo	numeric	4 unique values 0 missing
vertic	numeric	3 unique values 0 missing
illustr	numeric	2 unique values 0 missing
base	numeric	4 unique values 0 missing
isol	numeric	4 unique values 0 missing
ig	numeric	9 unique values 0 missing
phenomenon	numeric	4 unique values 0 missing
exposur	numeric	7 unique values 0 missing
trypsin	numeric	3 unique values 0 missing
predict	numeric	6 unique values 0 missing
estrogen	numeric	6 unique values 0 missing
dilat	numeric	3 unique values 0 missing
scatter	numeric	4 unique values 0 missing
aris	numeric	2 unique values 0 missing
naive	numeric	3 unique values 0 missing
basi	numeric	3 unique values 0 missing
isom	numeric	2 unique values 0 missing
distress	numeric	3 unique values 0 missing
difficult	numeric	2 unique values 0 missing
nucleotidas	numeric	3 unique values 0 missing
tracer	numeric	3 unique values 0 missing
mrna	numeric	2 unique values 0 missing
prepar	numeric	5 unique values 0 missing
childhood	numeric	4 unique values 0 missing
polyacrylamid	numeric	2 unique values 0 missing
clos	numeric	5 unique values 0 missing
anastomos	numeric	3 unique values 0 missing
grade	numeric	6 unique values 0 missing
osseou	numeric	3 unique values 0 missing
osmot	numeric	5 unique values 0 missing
male	numeric	6 unique values 0 missing
transform	numeric	4 unique values 0 missing
distal	numeric	4 unique values 0 missing
led	numeric	3 unique values 0 missing
symptomat	numeric	4 unique values 0 missing
agglutinin	numeric	2 unique values 0 missing
cascad	numeric	3 unique values 0 missing
bolu	numeric	4 unique values 0 missing
anastomot	numeric	4 unique values 0 missing
conceiv	numeric	2 unique values 0 missing
endocard	numeric	4 unique values 0 missing
ii	numeric	7 unique values 0 missing
conserv	numeric	4 unique values 0 missing
ria	numeric	3 unique values 0 missing
forti	numeric	2 unique values 0 missing
leg	numeric	3 unique values 0 missing
vocal	numeric	4 unique values 0 missing
flexion	numeric	4 unique values 0 missing
greatli	numeric	2 unique values 0 missing
clot	numeric	4 unique values 0 missing
inher	numeric	2 unique values 0 missing
constant	numeric	3 unique values 0 missing
prolong	numeric	6 unique values 0 missing
offer	numeric	3 unique values 0 missing
impress	numeric	2 unique values 0 missing
rib	numeric	4 unique values 0 missing
electrolyt	numeric	3 unique values 0 missing
intracoronari	numeric	2 unique values 0 missing
chimer	numeric	4 unique values 0 missing
tibial	numeric	3 unique values 0 missing
incomplet	numeric	2 unique values 0 missing
avoid	numeric	4 unique values 0 missing
rectal	numeric	3 unique values 0 missing
prior	numeric	3 unique values 0 missing
mitochondria	numeric	6 unique values 0 missing
importantli	numeric	2 unique values 0 missing
monte	numeric	3 unique values 0 missing
autoimmun	numeric	5 unique values 0 missing
thromboembol	numeric	6 unique values 0 missing
favor	numeric	3 unique values 0 missing
atherosclerot	numeric	3 unique values 0 missing
immunodefici	numeric	5 unique values 0 missing
sequenti	numeric	3 unique values 0 missing
mmol	numeric	4 unique values 0 missing
support	numeric	5 unique values 0 missing
time	numeric	6 unique values 0 missing
fibrinogen	numeric	3 unique values 0 missing
create	numeric	3 unique values 0 missing
month	numeric	7 unique values 0 missing
probabl	numeric	3 unique values 0 missing
remiss	numeric	3 unique values 0 missing
due	numeric	4 unique values 0 missing
anthropometr	numeric	4 unique values 0 missing
potent	numeric	3 unique values 0 missing
isonitrogen	numeric	3 unique values 0 missing
pathogenesi	numeric	3 unique values 0 missing
commonli	numeric	2 unique values 0 missing
glucagon	numeric	4 unique values 0 missing
stop	numeric	2 unique values 0 missing
underscor	numeric	2 unique values 0 missing
emuls	numeric	4 unique values 0 missing
cgy	numeric	3 unique values 0 missing
motion	numeric	5 unique values 0 missing
intravascular	numeric	3 unique values 0 missing
fill	numeric	3 unique values 0 missing
kcal	numeric	5 unique values 0 missing
nad	numeric	3 unique values 0 missing
titrat	numeric	2 unique values 0 missing
carotid	numeric	8 unique values 0 missing
rectum	numeric	3 unique values 0 missing
correl	numeric	7 unique values 0 missing
il	numeric	5 unique values 0 missing
mother	numeric	5 unique values 0 missing
postinject	numeric	2 unique values 0 missing
dental	numeric	5 unique values 0 missing
len	numeric	3 unique values 0 missing
independ	numeric	4 unique values 0 missing
woman	numeric	3 unique values 0 missing
probe	numeric	5 unique values 0 missing
util	numeric	4 unique values 0 missing
catecholamin	numeric	3 unique values 0 missing

Show first 100 features

107 properties

NumberOfInstances

918

Number of instances (rows) of the dataset.

NumberOfFeatures

3013

Number of attributes (columns) of the dataset.

NumberOfClasses

Number of distinct values of the target attribute (if it is nominal).

NumberOfMissingValues

Number of missing values in the dataset.

NumberOfInstancesWithMissingValues

Number of instances with at least one value missing.

NumberOfNumericFeatures

3012

Number of numeric attributes.

NumberOfSymbolicFeatures

Number of nominal attributes.

Quartile1SkewnessOfNumericAtts

10.26

First quartile of skewness among attributes of the numeric type.

REPTreeDepth1Kappa

0.75

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_kNN1NAUC

0.93

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

StdvNominalAttDistinctValues

Standard deviation of the number of distinct values among attributes of the nominal type.

J48.001.ErrRate

0.21

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanNominalAttDistinctValues

Average number of distinct values among the attributes of the nominal type.

Quartile1StdDevOfNumericAtts

0.09

First quartile of standard deviation of attributes of the numeric type.

REPTreeDepth2AUC

0.94

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

CfsSubsetEval_kNN1NErrRate

0.24

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

kNN1NAUC

0.54

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

J48.001.Kappa

0.77

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanSkewnessOfNumericAtts

14.46

Mean skewness among attributes of the numeric type.

Quartile2AttributeEntropy

Second quartile (Median) of entropy among attributes.

REPTreeDepth2ErrRate

0.22

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2

CfsSubsetEval_kNN1NKappa

0.73

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

kNN1NErrRate

0.82

Error rate achieved by the landmarker weka.classifiers.lazy.IBk

MajorityClassPercentage

16.23

Percentage of instances belonging to the most frequent class.

MeanStdDevOfNumericAtts

0.22

Mean standard deviation of attributes of the numeric type.

Quartile2KurtosisOfNumericAtts

225.74

Second quartile (Median) of kurtosis among attributes of the numeric type.

REPTreeDepth2Kappa

0.75

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

ClassEntropy

3.24

Entropy of the target attribute values.

kNN1NKappa

0.07

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

MajorityClassSize

149

Number of instances belonging to the most frequent class.

MinAttributeEntropy

Minimal entropy among attributes.

Quartile2MeansOfNumericAtts

0.01

Second quartile (Median) of means among attributes of the numeric type.

REPTreeDepth3AUC

0.94

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpAUC

0.61

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxAttributeEntropy

Maximum entropy among attributes.

MinKurtosisOfNumericAtts

4.96

Minimum kurtosis among attributes of the numeric type.

Quartile2MutualInformation

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

REPTreeDepth3ErrRate

0.22

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpErrRate

0.75

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxKurtosisOfNumericAtts

901.89

Maximum kurtosis among attributes of the numeric type.

MinMeansOfNumericAtts

Minimum of means among attributes of the numeric type.

Quartile2SkewnessOfNumericAtts

14.51

Second quartile (Median) of skewness among attributes of the numeric type.

REPTreeDepth3Kappa

0.75

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpKappa

0.11

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxMeansOfNumericAtts

1.7

Maximum of means among attributes of the numeric type.

MinMutualInformation

Minimal mutual information between the nominal attributes and the target attribute.

PercentageOfBinaryFeatures

Percentage of binary attributes.

Quartile2StdDevOfNumericAtts

0.16

Second quartile (Median) of standard deviation of attributes of the numeric type.

RandomTreeDepth1AUC

0.62

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Dimensionality

3.28

Number of attributes divided by the number of instances.

MaxMutualInformation

Maximum mutual information between the nominal attributes and the target attribute.

MinNominalAttDistinctValues

The minimal number of distinct values among attributes of the nominal type.

PercentageOfInstancesWithMissingValues

Percentage of instances having missing values.

Quartile3AttributeEntropy

Third quartile of entropy among attributes.

RandomTreeDepth1ErrRate

0.69

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

EquivalentNumberOfAtts

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

MaxNominalAttDistinctValues

The maximum number of distinct values among attributes of the nominal type.

MinSkewnessOfNumericAtts

1.85

Minimum skewness among attributes of the numeric type.

PercentageOfMissingValues

Percentage of missing values.

Quartile3KurtosisOfNumericAtts

353.17

Third quartile of kurtosis among attributes of the numeric type.

AutoCorrelation

0.23

Average class difference between consecutive instances.

RandomTreeDepth1Kappa

0.23

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

J48.00001.AUC

0.89

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MaxSkewnessOfNumericAtts

29.92

Maximum skewness among attributes of the numeric type.

MinStdDevOfNumericAtts

0.06

Minimum standard deviation of attributes of the numeric type.

PercentageOfNumericFeatures

99.97

Percentage of numeric attributes.

Quartile3MeansOfNumericAtts

0.03

Third quartile of means among attributes of the numeric type.

CfsSubsetEval_DecisionStumpAUC

0.93

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

RandomTreeDepth2AUC

0.62

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.00001.ErrRate

0.21

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MaxStdDevOfNumericAtts

3.71

Maximum standard deviation of attributes of the numeric type.

MinorityClassPercentage

6.43

Percentage of instances belonging to the least frequent class.

PercentageOfSymbolicFeatures

0.03

Percentage of nominal attributes.

Quartile3MutualInformation

Third quartile of mutual information between the nominal attributes and the target attribute.

CfsSubsetEval_DecisionStumpErrRate

0.24

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

RandomTreeDepth2ErrRate

0.69

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.00001.Kappa

0.77

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MeanAttributeEntropy

Average entropy of the attributes.

MinorityClassSize

Number of instances belonging to the least frequent class.

Quartile1AttributeEntropy

First quartile of entropy among attributes.

Quartile3SkewnessOfNumericAtts

17.94

Third quartile of skewness among attributes of the numeric type.

CfsSubsetEval_DecisionStumpKappa

0.73

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

RandomTreeDepth2Kappa

0.23

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.0001.AUC

0.89

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanKurtosisOfNumericAtts

260.51

Mean kurtosis among attributes of the numeric type.

NaiveBayesAUC

0.93

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1KurtosisOfNumericAtts

117.76

First quartile of kurtosis among attributes of the numeric type.

Quartile3StdDevOfNumericAtts

0.27

Third quartile of standard deviation of attributes of the numeric type.

CfsSubsetEval_NaiveBayesAUC

0.93

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

RandomTreeDepth3AUC

0.62

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.0001.ErrRate

0.21

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanMeansOfNumericAtts

0.03

Mean of means among attributes of the numeric type.

NaiveBayesErrRate

0.28

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1MeansOfNumericAtts

0.01

First quartile of means among attributes of the numeric type.

REPTreeDepth1AUC

0.94

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_NaiveBayesErrRate

0.24

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

RandomTreeDepth3ErrRate

0.69

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.0001.Kappa

0.77

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanMutualInformation

Average mutual information between the nominal attributes and the target attribute.

NaiveBayesKappa

0.69

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1MutualInformation

First quartile of mutual information between the nominal attributes and the target attribute.

REPTreeDepth1ErrRate

0.22

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_NaiveBayesKappa

0.73

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

RandomTreeDepth3Kappa

0.23

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.001.AUC

0.89

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanNoiseToSignalRatio

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

NumberOfBinaryFeatures

Number of binary attributes.

Show all 107 properties

62 tasks

Supervised Classification on oh5.wc

242 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CLASS_LABEL

Supervised Classification on oh5.wc

92 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CLASS_LABEL

Supervised Regression on oh5.wc

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class

Supervised Regression on oh5.wc

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: class

Supervised Regression on oh5.wc

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class

Supervised Regression on oh5.wc

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class

Supervised Regression on oh5.wc

0 runs - estimation_procedure: Custom 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class

Supervised Regression on oh5.wc

0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: class

Supervised Regression on oh5.wc

0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class

Learning Curve on oh5.wc

39 runs - estimation_procedure: 10-fold Learning Curve - target_feature: CLASS_LABEL

Supervised Data Stream Classification on oh5.wc

0 runs - estimation_procedure: Interleaved Test then Train - target_feature: CLASS_LABEL

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on oh5.wc

0 runs - estimation_procedure: 50 times Clustering

Subgroup Discovery on oh5.wc