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STL-10

STL-10

active ARFF Public Domain (CC0) Visibility: public Uploaded 23-06-2018 by Hall
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CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods. Overview 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck. Images are 96x96 pixels, color. 500 training images (10 pre-defined folds), 800 test images per class. 100000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. For instance, it contains other types of animals (bears, rabbits, etc.) and vehicles (trains, buses, etc.) in addition to the ones in the labeled set. Images were acquired from labeled examples on ImageNet.

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Show first 100 features

62 properties

NumberOfInstances
13000
Number of instances (rows) of the dataset.
NumberOfFeatures
27649
Number of attributes (columns) of the dataset.
NumberOfClasses
10
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
27648
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.91
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
102.69
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.11
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
63.05
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.82
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
110.21
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
0.21
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
64.6
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.72
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
118.26
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
0.34
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
68.55
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
109.85
Mean of means among attributes of the numeric type.
ClassEntropy
3.32
Entropy of the target attribute values.
Dimensionality
2.13
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.
MajorityClassPercentage
10
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1300
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
-0.3
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
128.44
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.7
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
81.53
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.82
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
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
10
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.24
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
66.2
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.17
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
82.47
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
10
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.04
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
59.31
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
10
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1300
Number of instances belonging to the least frequent class.
Show all 62 properties

15 tasks

Supervised Classification on STL-10
40 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Learning Curve on STL-10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on STL-10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on STL-10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on STL-10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on STL-10
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Clustering on STL-10
0 runs - estimation_procedure: 50 times Clustering
Define a new task