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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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing

62 properties

60000
Number of instances (rows) of the dataset.
3073
Number of attributes (columns) of the dataset.
10
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.
3072
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
61.54
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.32
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-0.39
Maximum kurtosis among attributes of the numeric type.
99.98
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-0.65
Third quartile of kurtosis among attributes of the numeric type.
140.56
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.97
Percentage of numeric attributes.
127.96
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
0.03
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
-0.08
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.26
Third quartile of skewness among attributes of the numeric type.
0.55
Maximum skewness among attributes of the numeric type.
57.46
Minimum standard deviation of attributes of the numeric type.
-0.85
First quartile of kurtosis among attributes of the numeric type.
65.19
Third quartile of standard deviation of attributes of the numeric type.
80.4
Maximum standard deviation of attributes of the numeric type.
10
Percentage of instances belonging to the least frequent class.
114.15
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.
6000
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.76
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.09
First quartile of skewness among attributes of the numeric type.
120.84
Mean of means among attributes of the numeric type.
60.02
First quartile of standard deviation of attributes of the numeric type.
0.1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
3.32
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.
-0.73
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
121.73
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.
0.2
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
10
Percentage of instances belonging to the most frequent class.
63.29
Mean standard deviation of attributes of the numeric type.
0.17
Second quartile (Median) of skewness among attributes of the numeric type.
6000
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

10 tasks

76 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - 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: 50 times Clustering
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