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la2s.wc

active Sparse_ARFF Publicly available Visibility: public Uploaded 28-09-2014 by unknown
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12433 features

CLASS_LABEL (target)	nominal	6 unique values 0 missing
transmiss	numeric	7 unique values 0 missing
translat	numeric	3 unique values 0 missing
transmit	numeric	4 unique values 0 missing
transmitt	numeric	2 unique values 0 missing
transpar	numeric	2 unique values 0 missing
transplant	numeric	4 unique values 0 missing
transport	numeric	10 unique values 0 missing
trap	numeric	6 unique values 0 missing
trash	numeric	8 unique values 0 missing
trauma	numeric	3 unique values 0 missing
traumat	numeric	2 unique values 0 missing
travail	numeric	2 unique values 0 missing
travel	numeric	6 unique values 0 missing
travers	numeric	2 unique values 0 missing
travi	numeric	4 unique values 0 missing
tre	numeric	2 unique values 0 missing
treacher	numeric	2 unique values 0 missing
tread	numeric	2 unique values 0 missing
treasur	numeric	3 unique values 0 missing
treasuri	numeric	9 unique values 0 missing
treate	numeric	5 unique values 0 missing
treati	numeric	3 unique values 0 missing
treatment	numeric	9 unique values 0 missing
tree	numeric	6 unique values 0 missing
trek	numeric	2 unique values 0 missing
tremble	numeric	2 unique values 0 missing
tremend	numeric	3 unique values 0 missing
tremor	numeric	2 unique values 0 missing
trench	numeric	2 unique values 0 missing
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trepid	numeric	2 unique values 0 missing
trespass	numeric	4 unique values 0 missing
trevino	numeric	2 unique values 0 missing
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tri	numeric	7 unique values 0 missing
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worthwhil	numeric	2 unique values 0 missing
worthless	numeric	2 unique values 0 missing
worthi	numeric	3 unique values 0 missing
worth	numeric	5 unique values 0 missing
worst	numeric	5 unique values 0 missing
worship	numeric	4 unique values 0 missing
worsen	numeric	3 unique values 0 missing
worse	numeric	3 unique values 0 missing
worrisom	numeric	3 unique values 0 missing
worri	numeric	5 unique values 0 missing
worn	numeric	3 unique values 0 missing
worldwid	numeric	4 unique values 0 missing
world	numeric	11 unique values 0 missing
workstat	numeric	2 unique values 0 missing
workshop	numeric	4 unique values 0 missing
workplac	numeric	6 unique values 0 missing
workout	numeric	5 unique values 0 missing
workload	numeric	2 unique values 0 missing
worker	numeric	17 unique values 0 missing
workdai	numeric	3 unique values 0 missing
workahol	numeric	2 unique values 0 missing
wore	numeric	4 unique values 0 missing
word	numeric	7 unique values 0 missing
woolridg	numeric	2 unique values 0 missing
woolf	numeric	3 unique values 0 missing
wool	numeric	3 unique values 0 missing
woodward	numeric	3 unique values 0 missing
woodson	numeric	3 unique values 0 missing
woodrow	numeric	3 unique values 0 missing
woodland	numeric	4 unique values 0 missing
woodi	numeric	4 unique values 0 missing
wooden	numeric	4 unique values 0 missing
woodbridg	numeric	9 unique values 0 missing
wood	numeric	6 unique values 0 missing
woo	numeric	5 unique values 0 missing
wong	numeric	4 unique values 0 missing
wonderland	numeric	2 unique values 0 missing
wonderfulli	numeric	3 unique values 0 missing
wonder	numeric	6 unique values 0 missing
won	numeric	12 unique values 0 missing
women	numeric	17 unique values 0 missing
woman	numeric	7 unique values 0 missing
wolverin	numeric	3 unique values 0 missing
wolve	numeric	3 unique values 0 missing
wolfsheim	numeric	6 unique values 0 missing
wolfpack	numeric	4 unique values 0 missing
wolfgang	numeric	2 unique values 0 missing
wolfe	numeric	3 unique values 0 missing
wolf	numeric	5 unique values 0 missing
woke	numeric	2 unique values 0 missing
wojciechowski	numeric	3 unique values 0 missing
woe	numeric	3 unique values 0 missing
wizard	numeric	2 unique values 0 missing
wive	numeric	3 unique values 0 missing
witti	numeric	2 unique values 0 missing
witter	numeric	3 unique values 0 missing
withstood	numeric	2 unique values 0 missing
withstand	numeric	2 unique values 0 missing
withhold	numeric	6 unique values 0 missing
withheld	numeric	3 unique values 0 missing
withdrew	numeric	4 unique values 0 missing
withdrawn	numeric	3 unique values 0 missing
withdraw	numeric	7 unique values 0 missing
wit	numeric	9 unique values 0 missing
wishe	numeric	3 unique values 0 missing
wish	numeric	5 unique values 0 missing
wiser	numeric	3 unique values 0 missing
wise	numeric	3 unique values 0 missing
wisdom	numeric	3 unique values 0 missing
wisconsin	numeric	4 unique values 0 missing
wirth	numeric	3 unique values 0 missing
wire	numeric	4 unique values 0 missing
wir	numeric	4 unique values 0 missing
wipe	numeric	2 unique values 0 missing
wip	numeric	2 unique values 0 missing
wintri	numeric	2 unique values 0 missing
wintern	numeric	4 unique values 0 missing
winter	numeric	10 unique values 0 missing
winston	numeric	3 unique values 0 missing
winnipeg	numeric	3 unique values 0 missing
winnie	numeric	4 unique values 0 missing
winner	numeric	8 unique values 0 missing
winless	numeric	3 unique values 0 missing
winkler	numeric	2 unique values 0 missing
wink	numeric	3 unique values 0 missing
winger	numeric	3 unique values 0 missing
wing	numeric	5 unique values 0 missing
winfrei	numeric	3 unique values 0 missing
wineri	numeric	4 unique values 0 missing
wine	numeric	8 unique values 0 missing
windshield	numeric	3 unique values 0 missing
windowless	numeric	2 unique values 0 missing
window	numeric	5 unique values 0 missing
windmill	numeric	3 unique values 0 missing
windfall	numeric	3 unique values 0 missing
wind	numeric	6 unique values 0 missing
win	numeric	12 unique values 0 missing
wimp	numeric	3 unique values 0 missing
wimbledon	numeric	3 unique values 0 missing
wilt	numeric	2 unique values 0 missing
wilson	numeric	12 unique values 0 missing
wilshir	numeric	4 unique values 0 missing
wilmington	numeric	3 unique values 0 missing
willow	numeric	2 unique values 0 missing
willman	numeric	3 unique values 0 missing
willingli	numeric	2 unique values 0 missing
willing	numeric	3 unique values 0 missing
willie	numeric	5 unique values 0 missing
williamson	numeric	3 unique values 0 missing
william	numeric	11 unique values 0 missing
willi	numeric	3 unique values 0 missing
willei	numeric	2 unique values 0 missing
willard	numeric	2 unique values 0 missing
will	numeric	4 unique values 0 missing
wilkinson	numeric	3 unique values 0 missing
wilkin	numeric	5 unique values 0 missing
wilken	numeric	4 unique values 0 missing
wili	numeric	4 unique values 0 missing
wilhit	numeric	5 unique values 0 missing
wilhelm	numeric	2 unique values 0 missing
wildlif	numeric	4 unique values 0 missing
wildli	numeric	2 unique values 0 missing
wilder	numeric	4 unique values 0 missing
wildcat	numeric	6 unique values 0 missing
wild	numeric	9 unique values 0 missing
wilburi	numeric	3 unique values 0 missing
wilbur	numeric	3 unique values 0 missing
wiggin	numeric	4 unique values 0 missing
wig	numeric	2 unique values 0 missing
wife	numeric	9 unique values 0 missing
wield	numeric	3 unique values 0 missing
widow	numeric	4 unique values 0 missing
widest	numeric	2 unique values 0 missing
widespread	numeric	3 unique values 0 missing
wider	numeric	3 unique values 0 missing
widen	numeric	4 unique values 0 missing
wide	numeric	4 unique values 0 missing
wichita	numeric	4 unique values 0 missing
wi	numeric	5 unique values 0 missing
whop	numeric	2 unique values 0 missing
whoop	numeric	2 unique values 0 missing
whom	numeric	4 unique values 0 missing
wholli	numeric	2 unique values 0 missing
wholesal	numeric	3 unique values 0 missing
whoever	numeric	2 unique values 0 missing
whize	numeric	2 unique values 0 missing
whittl	numeric	2 unique values 0 missing
whittier	numeric	5 unique values 0 missing
whitnei	numeric	3 unique values 0 missing
whitmor	numeric	3 unique values 0 missing
whitewash	numeric	2 unique values 0 missing
white	numeric	18 unique values 0 missing
whistle	numeric	3 unique values 0 missing
whistl	numeric	2 unique values 0 missing
whisper	numeric	3 unique values 0 missing
whiskei	numeric	3 unique values 0 missing
whirlwind	numeric	2 unique values 0 missing
whirl	numeric	2 unique values 0 missing
whip	numeric	3 unique values 0 missing
whimsi	numeric	2 unique values 0 missing
whim	numeric	3 unique values 0 missing
whichev	numeric	2 unique values 0 missing
wherewith	numeric	2 unique values 0 missing
wherev	numeric	2 unique values 0 missing
whereupon	numeric	2 unique values 0 missing
wherein	numeric	3 unique values 0 missing
wang	numeric	3 unique values 0 missing
videotap	numeric	5 unique values 0 missing
vie	numeric	2 unique values 0 missing
viejo	numeric	10 unique values 0 missing
vienna	numeric	5 unique values 0 missing
viet	numeric	3 unique values 0 missing
vietnam	numeric	8 unique values 0 missing
vietnames	numeric	9 unique values 0 missing
view	numeric	10 unique values 0 missing
viewer	numeric	5 unique values 0 missing
viewpoint	numeric	4 unique values 0 missing
vigil	numeric	3 unique values 0 missing
vignett	numeric	2 unique values 0 missing
vigor	numeric	3 unique values 0 missing
vik	numeric	5 unique values 0 missing
villa	numeric	4 unique values 0 missing
villag	numeric	8 unique values 0 missing
villain	numeric	3 unique values 0 missing
villanova	numeric	3 unique values 0 missing
vin	numeric	2 unique values 0 missing
vince	numeric	2 unique values 0 missing
vincent	numeric	2 unique values 0 missing
vindic	numeric	3 unique values 0 missing
vine	numeric	2 unique values 0 missing
vineyard	numeric	3 unique values 0 missing
vinogradov	numeric	4 unique values 0 missing
vintag	numeric	4 unique values 0 missing
vinyl	numeric	4 unique values 0 missing
viola	numeric	2 unique values 0 missing
violat	numeric	9 unique values 0 missing
violenc	numeric	7 unique values 0 missing
violent	numeric	4 unique values 0 missing
violet	numeric	3 unique values 0 missing
violin	numeric	5 unique values 0 missing
violinist	numeric	4 unique values 0 missing
violist	numeric	3 unique values 0 missing
viral	numeric	2 unique values 0 missing
virgin	numeric	3 unique values 0 missing
virginia	numeric	5 unique values 0 missing
virtual	numeric	4 unique values 0 missing
virtue	numeric	3 unique values 0 missing
virtuos	numeric	2 unique values 0 missing
virtuoso	numeric	2 unique values 0 missing
viru	numeric	12 unique values 0 missing
virul	numeric	3 unique values 0 missing
virus	numeric	4 unique values 0 missing
visa	numeric	5 unique values 0 missing
viscer	numeric	2 unique values 0 missing
visibl	numeric	4 unique values 0 missing
visibli	numeric	2 unique values 0 missing
vision	numeric	4 unique values 0 missing
visionari	numeric	2 unique values 0 missing
visit	numeric	8 unique values 0 missing
visitor	numeric	4 unique values 0 missing
vista	numeric	14 unique values 0 missing
visual	numeric	3 unique values 0 missing
vita;	numeric	2 unique values 0 missing
vital	numeric	4 unique values 0 missing
vitti	numeric	3 unique values 0 missing
vivid	numeric	2 unique values 0 missing
vo	numeric	4 unique values 0 missing
vocabulari	numeric	3 unique values 0 missing
vocal	numeric	7 unique values 0 missing
vocalist	numeric	3 unique values 0 missing
vocat	numeric	3 unique values 0 missing
vodka	numeric	2 unique values 0 missing
vogel	numeric	3 unique values 0 missing
voi	numeric	4 unique values 0 missing
voic	numeric	3 unique values 0 missing
voice	numeric	6 unique values 0 missing
void	numeric	3 unique values 0 missing
volatil	numeric	3 unique values 0 missing
volcan	numeric	2 unique values 0 missing
volkswagen	numeric	4 unique values 0 missing
vollei	numeric	3 unique values 0 missing
volleybal	numeric	7 unique values 0 missing
vollmer	numeric	2 unique values 0 missing
voltag	numeric	2 unique values 0 missing
volum	numeric	6 unique values 0 missing
volunt	numeric	10 unique values 0 missing
voluntari	numeric	5 unique values 0 missing
voluntarili	numeric	3 unique values 0 missing
volvo	numeric	2 unique values 0 missing
vomit	numeric	2 unique values 0 missing
von	numeric	6 unique values 0 missing
voss	numeric	4 unique values 0 missing
vot	numeric	8 unique values 0 missing
vote	numeric	15 unique values 0 missing
voter	numeric	10 unique values 0 missing
vow	numeric	3 unique values 0 missing
voyag	numeric	5 unique values 0 missing
vreni	numeric	4 unique values 0 missing
vt	numeric	3 unique values 0 missing
vulgar	numeric	2 unique values 0 missing
vulner	numeric	4 unique values 0 missing
vy	numeric	3 unique values 0 missing
waad	numeric	2 unique values 0 missing
wac	numeric	5 unique values 0 missing
wacki	numeric	3 unique values 0 missing
wade	numeric	5 unique values 0 missing
wadie	numeric	2 unique values 0 missing
wag	numeric	2 unique values 0 missing
wage	numeric	8 unique values 0 missing
wager	numeric	4 unique values 0 missing
wagner	numeric	6 unique values 0 missing
wagon	numeric	4 unique values 0 missing
wail	numeric	2 unique values 0 missing
waist	numeric	2 unique values 0 missing
waistband	numeric	2 unique values 0 missing
wait	numeric	6 unique values 0 missing
waite	numeric	3 unique values 0 missing
waiter	numeric	4 unique values 0 missing
waitress	numeric	4 unique values 0 missing
waiv	numeric	2 unique values 0 missing
waive	numeric	2 unique values 0 missing
waiver	numeric	4 unique values 0 missing
wak	numeric	2 unique values 0 missing
wake	numeric	4 unique values 0 missing
wale	numeric	5 unique values 0 missing
walesa	numeric	5 unique values 0 missing
walk	numeric	7 unique values 0 missing
walker	numeric	8 unique values 0 missing
walkout	numeric	4 unique values 0 missing
wall	numeric	8 unique values 0 missing
wallac	numeric	4 unique values 0 missing
wallet	numeric	3 unique values 0 missing
walli	numeric	4 unique values 0 missing
wallis	numeric	3 unique values 0 missing
wallop	numeric	3 unique values 0 missing
wallpap	numeric	2 unique values 0 missing
walnut	numeric	4 unique values 0 missing
walsh	numeric	9 unique values 0 missing
walt	numeric	4 unique values 0 missing
walter	numeric	6 unique values 0 missing
walton	numeric	5 unique values 0 missing
wan	numeric	2 unique values 0 missing
wander	numeric	3 unique values 0 missing
wendt	numeric	3 unique values 0 missing
webber	numeric	2 unique values 0 missing
weber	numeric	4 unique values 0 missing
webster	numeric	4 unique values 0 missing
wed	numeric	4 unique values 0 missing
wedge	numeric	3 unique values 0 missing
wednesdai	numeric	9 unique values 0 missing
wee	numeric	3 unique values 0 missing
weed	numeric	2 unique values 0 missing
week	numeric	10 unique values 0 missing
weekdai	numeric	2 unique values 0 missing
weekend	numeric	8 unique values 0 missing
weekli	numeric	6 unique values 0 missing
weeklong	numeric	2 unique values 0 missing
weeknight	numeric	2 unique values 0 missing
weep	numeric	2 unique values 0 missing
weigh	numeric	4 unique values 0 missing
weight	numeric	5 unique values 0 missing
weightlift	numeric	3 unique values 0 missing
weinberg	numeric	2 unique values 0 missing
weinstein	numeric	3 unique values 0 missing
weintraub	numeric	3 unique values 0 missing
weird	numeric	2 unique values 0 missing
weiss	numeric	4 unique values 0 missing
welch	numeric	3 unique values 0 missing
welcom	numeric	6 unique values 0 missing
weld	numeric	2 unique values 0 missing
welfar	numeric	5 unique values 0 missing
welle	numeric	3 unique values 0 missing
welsh	numeric	3 unique values 0 missing
welterweight	numeric	5 unique values 0 missing
wen	numeric	2 unique values 0 missing
wendell	numeric	2 unique values 0 missing
wendi	numeric	3 unique values 0 missing
weyler	numeric	3 unique values 0 missing
whatev	numeric	4 unique values 0 missing
whaler	numeric	3 unique values 0 missing
whale	numeric	3 unique values 0 missing
whack	numeric	3 unique values 0 missing
wet	numeric	4 unique values 0 missing
westwood	numeric	6 unique values 0 missing
whatsoev	numeric	2 unique values 0 missing

Show first 100 features

107 properties

NumberOfInstances

3075

Number of instances (rows) of the dataset.

NumberOfFeatures

12433

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

12432

Number of numeric attributes.

NumberOfSymbolicFeatures

Number of nominal attributes.

AutoCorrelation

0.75

Average class difference between consecutive instances.

CfsSubsetEval_DecisionStumpAUC

0.67

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

0.53

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

0.25

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

0.92

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

0.32

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

0.61

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

0.87

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

0.23

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

0.71

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

2.43

Entropy of the target attribute values.

DecisionStumpAUC

0.67

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

DecisionStumpErrRate

0.53

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

DecisionStumpKappa

0.25

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

Dimensionality

4.04

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

0.87

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

J48.00001.ErrRate

0.27

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

J48.00001.Kappa

0.66

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

J48.0001.AUC

0.87

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

J48.0001.ErrRate

0.26

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

J48.0001.Kappa

0.67

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

J48.001.AUC

0.87

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

J48.001.ErrRate

0.25

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

J48.001.Kappa

0.68

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

MajorityClassPercentage

29.43

Percentage of instances belonging to the most frequent class.

MajorityClassSize

905

Number of instances belonging to the most frequent class.

MaxAttributeEntropy

Maximum entropy among attributes.

MaxKurtosisOfNumericAtts

3063.02

Maximum kurtosis among attributes of the numeric type.

MaxMeansOfNumericAtts

1.97

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

55.29

Maximum skewness among attributes of the numeric type.

MaxStdDevOfNumericAtts

2.72

Maximum standard deviation of attributes of the numeric type.

MeanAttributeEntropy

Average entropy of the attributes.

MeanKurtosisOfNumericAtts

774.24

Mean kurtosis among attributes of the numeric type.

MeanMeansOfNumericAtts

0.02

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

24.38

Mean skewness among attributes of the numeric type.

MeanStdDevOfNumericAtts

0.16

Mean standard deviation of attributes of the numeric type.

MinAttributeEntropy

Minimal entropy among attributes.

MinKurtosisOfNumericAtts

-1.65

Minimum kurtosis among attributes of the numeric type.

MinMeansOfNumericAtts

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

-3.68

Minimum skewness among attributes of the numeric type.

MinStdDevOfNumericAtts

0.03

Minimum standard deviation of attributes of the numeric type.

MinorityClassPercentage

8.07

Percentage of instances belonging to the least frequent class.

MinorityClassSize

248

Number of instances belonging to the least frequent class.

NaiveBayesAUC

0.92

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

NaiveBayesErrRate

0.25

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

NaiveBayesKappa

0.69

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

NumberOfBinaryFeatures

Number of binary attributes.

PercentageOfBinaryFeatures

Percentage of binary attributes.

PercentageOfInstancesWithMissingValues

Percentage of instances having missing values.

PercentageOfMissingValues

Percentage of missing values.

PercentageOfNumericFeatures

99.99

Percentage of numeric attributes.

PercentageOfSymbolicFeatures

0.01

Percentage of nominal attributes.

Quartile1AttributeEntropy

First quartile of entropy among attributes.

Quartile1KurtosisOfNumericAtts

304.36

First quartile of kurtosis among attributes of the numeric type.

Quartile1MeansOfNumericAtts

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

16.25

First quartile of skewness among attributes of the numeric type.

Quartile1StdDevOfNumericAtts

0.05

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

Quartile2AttributeEntropy

Second quartile (Median) of entropy among attributes.

Quartile2KurtosisOfNumericAtts

611

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

Quartile2MeansOfNumericAtts

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

24.5

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

Quartile2StdDevOfNumericAtts

0.08

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

Quartile3AttributeEntropy

Third quartile of entropy among attributes.

Quartile3KurtosisOfNumericAtts

1021.66

Third quartile of kurtosis among attributes of the numeric type.

Quartile3MeansOfNumericAtts

0.01

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

31.98

Third quartile of skewness among attributes of the numeric type.

Quartile3StdDevOfNumericAtts

0.18

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

REPTreeDepth1AUC

0.67

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

REPTreeDepth1ErrRate

0.53

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

REPTreeDepth1Kappa

0.25

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

REPTreeDepth2AUC

0.74

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

REPTreeDepth2ErrRate

0.5

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

REPTreeDepth2Kappa

0.33

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

REPTreeDepth3AUC

0.77

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

REPTreeDepth3ErrRate

0.47

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

REPTreeDepth3Kappa

0.36

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

RandomTreeDepth1AUC

0.5

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

RandomTreeDepth1ErrRate

0.71

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

0.54

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

RandomTreeDepth2ErrRate

0.69

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

RandomTreeDepth2Kappa

0.03

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

RandomTreeDepth3AUC

0.59

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

RandomTreeDepth3ErrRate

0.68

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

RandomTreeDepth3Kappa

0.05

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

0.62

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

kNN1NErrRate

0.64

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

kNN1NKappa

0.23

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

Show all 107 properties

47 tasks

Supervised Classification on la2s.wc

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

Supervised Classification on la2s.wc

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

Supervised Classification on la2s.wc

0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: CLASS_LABEL

Supervised Regression on la2s.wc

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

Supervised Regression on la2s.wc

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

Supervised Regression on la2s.wc

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

Supervised Regression on la2s.wc

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

Supervised Regression on la2s.wc

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

Supervised Regression on la2s.wc

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

Supervised Regression on la2s.wc

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

Learning Curve on la2s.wc

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

Supervised Data Stream Classification on la2s.wc

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

Clustering on la2s.wc

0 runs

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Clustering on la2s.wc

0 runs - estimation_procedure: 50 times Clustering

Subgroup Discovery on la2s.wc