Data
CIFAR_10

CIFAR_10

active ARFF Publicly available Visibility: public Uploaded 26-09-2017 by Joaquin Vanschoren
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Author: Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton Source: [University of Toronto](https://www.cs.toronto.edu/~kriz/cifar.html) - 2009 Please cite: Alex Krizhevsky (2009) Learning Multiple Layers of Features from Tiny Images, Tech Report. CIFAR-10 is a labeled subset of the [80 million tiny images dataset](http://groups.csail.mit.edu/vision/TinyImages/). It (originally) consists 32x32 color images representing 10 classes of objects: 0. airplane 1. automobile 2. bird 3. cat 4. deer 5. dog 6. frog 7. horse 8. ship 9. truck CIFAR-10 contains 6000 images per class. The original train-test split randomly divided these into 5000 train and 1000 test images per class. The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks. ### Attribute description Each instance represents a 32x32 colour image as a 3072-value array. The first 1024 entries contain the red channel values, the next 1024 the green, and the final 1024 the blue. The image is stored in row-major order, so that the first 32 entries of the array are the red channel values of the first row of the image. The labels are encoded as integers in the range 0-9, corresponding to the numbered classes listed above.

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Show first 100 features

62 properties

NumberOfInstances
60000
Number of instances (rows) of the dataset.
NumberOfFeatures
3073
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
3072
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanSkewnessOfNumericAtts
0.2
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
121.73
Second quartile (Median) of means among attributes of the numeric type.
MajorityClassPercentage
10
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
63.29
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassSize
6000
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
0.17
Second quartile (Median) of skewness among attributes of the numeric type.
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.32
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
61.54
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
-0.39
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
99.98
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
140.56
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
-0.65
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
10
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
99.97
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
127.96
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.08
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
0.03
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxSkewnessOfNumericAtts
0.55
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
57.46
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
0.26
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
80.4
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
10
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
-0.85
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
65.19
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
6000
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
114.15
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.
MeanKurtosisOfNumericAtts
-0.76
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
120.84
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
0.09
First quartile of skewness among attributes of the numeric type.
AutoCorrelation
0.1
Average class difference between consecutive instances.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
60.02
First quartile of standard deviation of attributes of the numeric type.
ClassEntropy
3.32
Entropy of the target attribute values.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Dimensionality
0.05
Number of attributes divided by the number of instances.
MeanNominalAttDistinctValues
10
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
-0.73
Second quartile (Median) of kurtosis among attributes of the numeric type.
Show all 62 properties

22 tasks

Supervised Classification on CIFAR_10
160 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on CIFAR_10
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on CIFAR_10
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on CIFAR_10
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on CIFAR_10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on CIFAR_10
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10
0 runs - estimation_procedure: 50 times Clustering
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