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Fashion-MNIST

Fashion-MNIST

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Author: Han Xiao, Kashif Rasul, Roland Vollgraf Source: [Zalando Research](https://github.com/zalandoresearch/fashion-mnist) Please cite: Han Xiao and Kashif Rasul and Roland Vollgraf, Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, arXiv, cs.LG/1708.07747 Fashion-MNIST is a dataset of Zalando's article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. Raw data available at: https://github.com/zalandoresearch/fashion-mnist ### Target classes Each training and test example is assigned to one of the following labels: Label Description 0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot

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Show first 100 features

62 properties

NumberOfInstances
70000
Number of instances (rows) of the dataset.
NumberOfFeatures
785
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
784
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
99.87
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.13
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.48
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
24.1
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
-0.05
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
57.74
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.79
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
76.12
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
0.66
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
81.32
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
4.47
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
112.82
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
2.36
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
90.55
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
72.97
Mean of means among attributes of the numeric type.
ClassEntropy
3.32
Entropy of the target attribute values.
Dimensionality
0.01
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
7000
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
26177.85
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
161.74
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
155.91
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
103.65
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
199.67
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
3.62
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
70.08
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.74
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
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
-0.87
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.09
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
10
Percentage of instances belonging to the least frequent class.
MinorityClassSize
7000
Number of instances belonging to the least frequent class.
Show all 62 properties

25 tasks

Supervised Classification on Fashion-MNIST
451 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on Fashion-MNIST
4 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on Fashion-MNIST
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on Fashion-MNIST
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on Fashion-MNIST
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
Supervised Classification on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on Fashion-MNIST
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on Fashion-MNIST
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on Fashion-MNIST
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on Fashion-MNIST
0 runs
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
Clustering on Fashion-MNIST
0 runs - estimation_procedure: 50 times Clustering
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