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sylva_agnostic

sylva_agnostic

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Author: Source: Unknown - Date unknown Please cite: Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch) Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php Modified by TunedIT (converted to ARFF format) SYLVA is the ecology database The task of SYLVA is to classify forest cover types. The forest cover type for 30 x 30 meter cells is obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. We brought it back to a two-class classification problem (classifying Ponderosa pine vs. everything else). The "agnostic learning track" data consists in 216 input variables. Each pattern is composed of 4 records: 2 true records matching the target and 2 records picked at random. Thus 1/2 of the features are distracters. Data type: non-sparse Number of features: 216 Number of examples and check-sums: Pos_ex Neg_ex Tot_ex Check_sum Train 805 12281 13086 238271607.00 Valid 81 1228 1309 23817234.00 This dataset contains samples from both training and validation datasets.

217 features

label (target)nominal2 unique values
0 missing
attr0numeric173 unique values
0 missing
attr1numeric2 unique values
0 missing
attr2numeric2 unique values
0 missing
attr3numeric2 unique values
0 missing
attr4numeric915 unique values
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attr5numeric2 unique values
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attr6numeric2 unique values
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attr7numeric2 unique values
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attr8numeric2 unique values
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attr9numeric923 unique values
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attr10numeric2 unique values
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attr11numeric354 unique values
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attr12numeric441 unique values
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attr13numeric2 unique values
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attr14numeric1 unique values
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attr16numeric2 unique values
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attr19numeric2 unique values
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attr20numeric353 unique values
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attr21numeric2 unique values
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attr22numeric165 unique values
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attr23numeric167 unique values
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attr24numeric2 unique values
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attr25numeric2 unique values
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attr27numeric2 unique values
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attr30numeric2 unique values
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attr32numeric2 unique values
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attr33numeric2 unique values
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attr34numeric2 unique values
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attr35numeric2 unique values
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attr36numeric2 unique values
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attr37numeric2 unique values
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attr42numeric2 unique values
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attr43numeric2 unique values
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attr44numeric2 unique values
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attr45numeric2 unique values
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attr46numeric2 unique values
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attr47numeric2 unique values
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attr48numeric129 unique values
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attr49numeric2 unique values
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attr50numeric2 unique values
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attr51numeric361 unique values
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attr52numeric2 unique values
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attr53numeric909 unique values
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attr54numeric803 unique values
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attr55numeric2 unique values
0 missing
attr56numeric2 unique values
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attr57numeric2 unique values
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attr58numeric2 unique values
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attr59numeric361 unique values
0 missing
attr60numeric2 unique values
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attr61numeric2 unique values
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attr62numeric245 unique values
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attr63numeric2 unique values
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attr64numeric2 unique values
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attr65numeric2 unique values
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attr66numeric2 unique values
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attr67numeric2 unique values
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attr68numeric2 unique values
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attr69numeric239 unique values
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attr70numeric2 unique values
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attr71numeric2 unique values
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attr72numeric2 unique values
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attr73numeric2 unique values
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attr74numeric131 unique values
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attr75numeric2 unique values
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attr76numeric2 unique values
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attr77numeric2 unique values
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attr78numeric2 unique values
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attr79numeric2 unique values
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attr80numeric2 unique values
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attr81numeric2 unique values
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attr82numeric2 unique values
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attr83numeric2 unique values
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attr84numeric2 unique values
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attr85numeric2 unique values
0 missing
attr86numeric2 unique values
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attr87numeric2 unique values
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attr88numeric2 unique values
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attr89numeric2 unique values
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attr90numeric2 unique values
0 missing
attr91numeric915 unique values
0 missing
attr92numeric1 unique values
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attr93numeric2 unique values
0 missing
attr94numeric2 unique values
0 missing
attr95numeric2 unique values
0 missing
attr96numeric799 unique values
0 missing
attr97numeric360 unique values
0 missing
attr98numeric2 unique values
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attr99numeric2 unique values
0 missing
attr100numeric2 unique values
0 missing
attr101numeric427 unique values
0 missing
attr102numeric2 unique values
0 missing
attr103numeric2 unique values
0 missing
attr104numeric2 unique values
0 missing
attr105numeric2 unique values
0 missing
attr106numeric916 unique values
0 missing
attr107numeric2 unique values
0 missing
attr108numeric2 unique values
0 missing
attr109numeric801 unique values
0 missing
attr110numeric2 unique values
0 missing
attr111numeric353 unique values
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attr112numeric2 unique values
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attr113numeric2 unique values
0 missing
attr114numeric2 unique values
0 missing
attr115numeric1 unique values
0 missing
attr116numeric2 unique values
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attr117numeric2 unique values
0 missing
attr118numeric2 unique values
0 missing
attr119numeric2 unique values
0 missing
attr120numeric2 unique values
0 missing
attr121numeric2 unique values
0 missing
attr122numeric2 unique values
0 missing
attr123numeric2 unique values
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attr124numeric2 unique values
0 missing
attr125numeric2 unique values
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attr126numeric2 unique values
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attr127numeric2 unique values
0 missing
attr128numeric2 unique values
0 missing
attr129numeric2 unique values
0 missing
attr130numeric2 unique values
0 missing
attr131numeric2 unique values
0 missing
attr132numeric2 unique values
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attr133numeric2 unique values
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attr134numeric2 unique values
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attr135numeric2 unique values
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attr136numeric2 unique values
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attr137numeric2 unique values
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attr138numeric2 unique values
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attr139numeric2 unique values
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attr140numeric2 unique values
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attr141numeric2 unique values
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attr142numeric242 unique values
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attr143numeric2 unique values
0 missing
attr144numeric2 unique values
0 missing
attr145numeric2 unique values
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attr146numeric340 unique values
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attr147numeric2 unique values
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attr148numeric906 unique values
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attr149numeric2 unique values
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attr150numeric2 unique values
0 missing
attr151numeric48 unique values
0 missing
attr152numeric49 unique values
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attr153numeric429 unique values
0 missing
attr154numeric2 unique values
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attr155numeric2 unique values
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attr156numeric2 unique values
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attr157numeric2 unique values
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attr158numeric2 unique values
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attr159numeric2 unique values
0 missing
attr160numeric2 unique values
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attr161numeric50 unique values
0 missing
attr162numeric2 unique values
0 missing
attr163numeric128 unique values
0 missing
attr164numeric51 unique values
0 missing
attr165numeric918 unique values
0 missing
attr166numeric2 unique values
0 missing
attr167numeric170 unique values
0 missing
attr168numeric2 unique values
0 missing
attr169numeric2 unique values
0 missing
attr170numeric2 unique values
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attr171numeric2 unique values
0 missing
attr172numeric2 unique values
0 missing
attr173numeric2 unique values
0 missing
attr174numeric2 unique values
0 missing
attr175numeric2 unique values
0 missing
attr176numeric360 unique values
0 missing
attr177numeric430 unique values
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attr178numeric2 unique values
0 missing
attr179numeric919 unique values
0 missing
attr180numeric2 unique values
0 missing
attr181numeric2 unique values
0 missing
attr182numeric2 unique values
0 missing
attr183numeric2 unique values
0 missing
attr184numeric2 unique values
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attr185numeric2 unique values
0 missing
attr186numeric2 unique values
0 missing
attr187numeric2 unique values
0 missing
attr188numeric2 unique values
0 missing
attr189numeric2 unique values
0 missing
attr190numeric2 unique values
0 missing
attr191numeric2 unique values
0 missing
attr192numeric2 unique values
0 missing
attr193numeric2 unique values
0 missing
attr194numeric133 unique values
0 missing
attr195numeric2 unique values
0 missing
attr196numeric2 unique values
0 missing
attr197numeric2 unique values
0 missing
attr198numeric2 unique values
0 missing
attr199numeric2 unique values
0 missing
attr200numeric2 unique values
0 missing
attr201numeric801 unique values
0 missing
attr202numeric2 unique values
0 missing
attr203numeric244 unique values
0 missing
attr204numeric2 unique values
0 missing
attr205numeric2 unique values
0 missing
attr206numeric2 unique values
0 missing
attr207numeric2 unique values
0 missing
attr208numeric1 unique values
0 missing
attr209numeric2 unique values
0 missing
attr210numeric2 unique values
0 missing
attr211numeric2 unique values
0 missing
attr212numeric2 unique values
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attr213numeric2 unique values
0 missing
attr214numeric2 unique values
0 missing
attr215numeric2 unique values
0 missing

107 properties

14395
Number of instances (rows) of the dataset.
217
Number of attributes (columns) of the dataset.
2
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.
216
Number of numeric attributes.
1
Number of nominal attributes.
0.88
Average class difference between consecutive instances.
0.97
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
0.01
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
0.92
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
0.97
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
0.01
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
0.92
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
0.97
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
0.01
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
0.92
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
0.33
Entropy of the target attribute values.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.06
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.02
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.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
93.85
Percentage of instances belonging to the most frequent class.
13509
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
14395
Maximum kurtosis among attributes of the numeric type.
879.13
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
119.98
Maximum skewness among attributes of the numeric type.
312.04
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
560.67
Mean kurtosis among attributes of the numeric type.
84.29
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.
2
Average number of distinct values among the attributes of the nominal type.
13.35
Mean skewness among attributes of the numeric type.
28.4
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.97
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.
2
The minimal number of distinct values among attributes of the nominal type.
-1.18
Minimum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
6.15
Percentage of instances belonging to the least frequent class.
886
Number of instances belonging to the least frequent class.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
Number of binary attributes.
0.46
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
99.54
Percentage of numeric attributes.
0.46
Percentage of nominal attributes.
First quartile of entropy among attributes.
5.41
First quartile of kurtosis among attributes of the numeric type.
0
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
2.62
First quartile of skewness among attributes of the numeric type.
0.06
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
36.96
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
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.
6.24
Second quartile (Median) of skewness among attributes of the numeric type.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
274.69
Third quartile of kurtosis among attributes of the numeric type.
0.1
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
16.63
Third quartile of skewness among attributes of the numeric type.
0.3
Third quartile of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.67
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.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.02
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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