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CIFAR_10_small

CIFAR_10_small

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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 small This is a 20,000 instance sample of the original CIFAR-10 dataset. Sampled randomly and stratified, with 2000 examples per class. Training and test set are merged. Find the corresponding task for the original train-test splits. 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 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. The original CIFAR-10 dataset contains 6000 images per class. The original train-test split randomly divided these into 5000 train and 1000 test images per class. ### 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
20000
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.
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
99.97
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.03
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.85
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
114.49
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
0.09
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
60.07
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.73
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
122.27
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
0.17
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
61.65
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.64
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
128.53
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
0.26
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
65.3
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
121.29
Mean of means among attributes of the numeric type.
ClassEntropy
3.32
Entropy of the target attribute values.
Dimensionality
0.15
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
10.16
Percentage of instances belonging to the most frequent class.
MajorityClassSize
2032
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
-0.38
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
141.67
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
0.56
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
80.56
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.76
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
0.2
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
63.35
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.33
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
99.7
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
-0.1
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
57.23
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.69
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1937
Number of instances belonging to the least frequent class.
Show all 62 properties

20 tasks

Supervised Classification on CIFAR_10_small
380 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
Supervised Classification on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Learning Curve on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on CIFAR_10_small
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on CIFAR_10_small
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on CIFAR_10_small
0 runs
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Clustering on CIFAR_10_small
0 runs - estimation_procedure: 50 times Clustering
Define a new task