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Convex-test

Convex-test

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Recognition of convex sets The convex sets consist of a single convex region with pixels of value 255. Candidate convex images were constructed by taking the intersection of a number of half-planes whose location and orientation were chosen uniformly at random. The number of intersecting half-planes was also sampled randomly according to a geometric distribution with parameter 0.195. A candidate convex image was rejected if there were less than 19 pixels in the convex region. Candidate non-convex images were constructed by taking the union of a random number of convex sets generated as above, but with the number of half-planes sampled from a geometric distribution with parameter 0.07 and with a minimum number of 10 pixels. The number of convex sets was sampled uniformly from 2 to 4. The candidate non-convex images were then tested by checking a convexity condition for every pair of pixels in the non-convex set. Those sets that failed the convexity test were added to the dataset. The parameters for generating the convex and non-convex sets were balanced to ensure that the mean number of pixels of value 255 is the same in the two datasets. #autoxgboost #autoweka

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a212numeric2 unique values
0 missing
a468numeric2 unique values
0 missing
a724numeric2 unique values
0 missing
a213numeric2 unique values
0 missing
a469numeric2 unique values
0 missing
a725numeric2 unique values
0 missing
a214numeric2 unique values
0 missing
a470numeric2 unique values
0 missing
a726numeric2 unique values
0 missing
a215numeric2 unique values
0 missing
a471numeric2 unique values
0 missing
a727numeric2 unique values
0 missing
a216numeric2 unique values
0 missing
a472numeric2 unique values
0 missing
a728numeric2 unique values
0 missing
a217numeric2 unique values
0 missing
a473numeric2 unique values
0 missing
a729numeric2 unique values
0 missing
a218numeric2 unique values
0 missing
a474numeric2 unique values
0 missing
a730numeric2 unique values
0 missing
a219numeric2 unique values
0 missing
a475numeric2 unique values
0 missing
a731numeric2 unique values
0 missing
a220numeric2 unique values
0 missing
a476numeric2 unique values
0 missing
a732numeric2 unique values
0 missing
a221numeric2 unique values
0 missing
a477numeric2 unique values
0 missing
a733numeric2 unique values
0 missing
a222numeric2 unique values
0 missing
a478numeric2 unique values
0 missing
a734numeric2 unique values
0 missing
a223numeric2 unique values
0 missing
a479numeric2 unique values
0 missing
a735numeric2 unique values
0 missing
a224numeric2 unique values
0 missing
a480numeric2 unique values
0 missing
a736numeric2 unique values
0 missing
a225numeric2 unique values
0 missing
a481numeric2 unique values
0 missing
a737numeric2 unique values
0 missing
a226numeric2 unique values
0 missing
a482numeric2 unique values
0 missing
a738numeric2 unique values
0 missing
a227numeric2 unique values
0 missing
a483numeric2 unique values
0 missing
a739numeric2 unique values
0 missing
a228numeric2 unique values
0 missing
a484numeric2 unique values
0 missing
a740numeric2 unique values
0 missing
a229numeric2 unique values
0 missing
a485numeric2 unique values
0 missing
a741numeric2 unique values
0 missing
a230numeric2 unique values
0 missing
a486numeric2 unique values
0 missing
a742numeric2 unique values
0 missing
a231numeric2 unique values
0 missing
a487numeric2 unique values
0 missing
a743numeric2 unique values
0 missing
a232numeric2 unique values
0 missing
a488numeric2 unique values
0 missing
a744numeric2 unique values
0 missing
a233numeric2 unique values
0 missing
a489numeric2 unique values
0 missing
a745numeric2 unique values
0 missing
a234numeric2 unique values
0 missing
a490numeric2 unique values
0 missing
a746numeric2 unique values
0 missing
a235numeric2 unique values
0 missing
a491numeric2 unique values
0 missing
a747numeric2 unique values
0 missing
a236numeric2 unique values
0 missing
a492numeric2 unique values
0 missing
a748numeric2 unique values
0 missing
a237numeric2 unique values
0 missing
a493numeric2 unique values
0 missing
a749numeric2 unique values
0 missing
a238numeric2 unique values
0 missing
a494numeric2 unique values
0 missing
a750numeric2 unique values
0 missing
a239numeric2 unique values
0 missing
a495numeric2 unique values
0 missing
a751numeric2 unique values
0 missing
a240numeric2 unique values
0 missing
a496numeric2 unique values
0 missing
a752numeric2 unique values
0 missing
a241numeric2 unique values
0 missing
a497numeric2 unique values
0 missing
a753numeric2 unique values
0 missing
a242numeric2 unique values
0 missing
a498numeric2 unique values
0 missing
a754numeric2 unique values
0 missing
a243numeric2 unique values
0 missing
a499numeric2 unique values
0 missing
a755numeric2 unique values
0 missing
a244numeric2 unique values
0 missing
a500numeric2 unique values
0 missing
a756numeric2 unique values
0 missing
a245numeric2 unique values
0 missing
a501numeric2 unique values
0 missing
a757numeric2 unique values
0 missing
a246numeric2 unique values
0 missing
a502numeric2 unique values
0 missing
a758numeric2 unique values
0 missing
a247numeric2 unique values
0 missing
a503numeric2 unique values
0 missing
a759numeric2 unique values
0 missing
a248numeric2 unique values
0 missing
a504numeric2 unique values
0 missing
a760numeric2 unique values
0 missing
a249numeric2 unique values
0 missing
a505numeric2 unique values
0 missing
a761numeric2 unique values
0 missing
a250numeric2 unique values
0 missing
a506numeric2 unique values
0 missing
a762numeric2 unique values
0 missing
a251numeric2 unique values
0 missing
a507numeric2 unique values
0 missing
a763numeric2 unique values
0 missing
a252numeric2 unique values
0 missing
a508numeric2 unique values
0 missing
a764numeric2 unique values
0 missing
a253numeric2 unique values
0 missing
a509numeric2 unique values
0 missing
a765numeric2 unique values
0 missing
a254numeric2 unique values
0 missing
a510numeric2 unique values
0 missing
a766numeric2 unique values
0 missing
a255numeric2 unique values
0 missing
a511numeric2 unique values
0 missing
a767numeric2 unique values
0 missing

62 properties

50000
Number of instances (rows) of the dataset.
785
Number of attributes (columns) of the dataset.
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.
784
Number of numeric attributes.
1
Number of nominal attributes.
0.2
First quartile of skewness among attributes of the numeric type.
0.39
Mean of means among attributes of the numeric type.
0.47
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.77
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.38
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.49
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
0.48
Mean standard deviation of attributes of the numeric type.
0.48
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.13
Percentage of binary attributes.
0.49
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-0.37
Maximum kurtosis among attributes of the numeric type.
0.23
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-1.42
Third quartile of kurtosis among attributes of the numeric type.
0.52
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.87
Percentage of numeric attributes.
0.45
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0.13
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.09
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.76
Third quartile of skewness among attributes of the numeric type.
1.28
Maximum skewness among attributes of the numeric type.
0.42
Minimum standard deviation of attributes of the numeric type.
-1.96
First quartile of kurtosis among attributes of the numeric type.
0.5
Third quartile of standard deviation of attributes of the numeric type.
0.5
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0.32
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-1.65
Mean kurtosis among attributes of the numeric type.

15 tasks

0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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