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Fashion-MNIST

Fashion-MNIST

active ARFF Publicly available Visibility: public Uploaded 20-12-2017 by Irfan Nur Afif
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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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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
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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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62 properties

70000
Number of instances (rows) of the dataset.
785
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.
784
Number of numeric attributes.
1
Number of nominal attributes.
7000
Number of instances belonging to the least frequent class.
24.1
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.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
199.67
Mean kurtosis among attributes of the numeric type.
-0.05
First quartile of skewness among attributes of the numeric type.
72.97
Mean of means among attributes of the numeric type.
57.74
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.
-0.79
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
76.12
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.
3.62
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.
70.08
Mean standard deviation of attributes of the numeric type.
0.66
Second quartile (Median) of skewness among attributes of the numeric type.
7000
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
81.32
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.74
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
26177.85
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
4.47
Third quartile of kurtosis among attributes of the numeric type.
161.74
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.87
Percentage of numeric attributes.
112.82
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.
0.13
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.
-0.87
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.36
Third quartile of skewness among attributes of the numeric type.
155.91
Maximum skewness among attributes of the numeric type.
0.09
Minimum standard deviation of attributes of the numeric type.
-1.48
First quartile of kurtosis among attributes of the numeric type.
90.55
Third quartile of standard deviation of attributes of the numeric type.
103.65
Maximum standard deviation of attributes of the numeric type.
10
Percentage of instances belonging to the least frequent class.

12 tasks

436 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - 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: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
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