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Kuzushiji-49

Kuzushiji-49

active ARFF CC BY-SA 4.0 Visibility: public Uploaded 24-07-2019 by Matthias Feurer
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Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage themachine learning community into the world of classical Japanese literature.

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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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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
pixel-0212numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0213numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0214numeric256 unique values
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
pixel-0222numeric256 unique values
0 missing
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0 missing
pixel-0734numeric256 unique values
0 missing
pixel-0223numeric256 unique values
0 missing
pixel-0479numeric256 unique values
0 missing
pixel-0735numeric256 unique values
0 missing
pixel-0224numeric256 unique values
0 missing
pixel-0480numeric256 unique values
0 missing
pixel-0736numeric256 unique values
0 missing
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0 missing
pixel-0481numeric256 unique values
0 missing
pixel-0737numeric256 unique values
0 missing
pixel-0226numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0227numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0228numeric256 unique values
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
pixel-0230numeric256 unique values
0 missing
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0 missing
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0 missing
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0 missing
pixel-0487numeric256 unique values
0 missing
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0 missing
pixel-0232numeric256 unique values
0 missing
pixel-0488numeric256 unique values
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
pixel-0491numeric256 unique values
0 missing
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0 missing
pixel-0236numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0237numeric256 unique values
0 missing
pixel-0493numeric256 unique values
0 missing
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0 missing
pixel-0238numeric256 unique values
0 missing
pixel-0494numeric256 unique values
0 missing
pixel-0750numeric256 unique values
0 missing
pixel-0239numeric256 unique values
0 missing
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0 missing
pixel-0751numeric256 unique values
0 missing
pixel-0240numeric256 unique values
0 missing
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0 missing
pixel-0752numeric256 unique values
0 missing
pixel-0241numeric256 unique values
0 missing
pixel-0497numeric256 unique values
0 missing
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0 missing
pixel-0242numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0243numeric256 unique values
0 missing
pixel-0499numeric256 unique values
0 missing
pixel-0755numeric256 unique values
0 missing
pixel-0244numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0245numeric256 unique values
0 missing
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0 missing
pixel-0246numeric256 unique values
0 missing
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0 missing
pixel-0503numeric256 unique values
0 missing
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0 missing
pixel-0248numeric256 unique values
0 missing
pixel-0504numeric256 unique values
0 missing
pixel-0760numeric256 unique values
0 missing
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0 missing
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0 missing
pixel-0761numeric256 unique values
0 missing
pixel-0250numeric256 unique values
0 missing
pixel-0506numeric256 unique values
0 missing
pixel-0762numeric256 unique values
0 missing
pixel-0251numeric256 unique values
0 missing
pixel-0507numeric256 unique values
0 missing
pixel-0763numeric256 unique values
0 missing
pixel-0252numeric256 unique values
0 missing
pixel-0508numeric256 unique values
0 missing
pixel-0764numeric256 unique values
0 missing
pixel-0253numeric256 unique values
0 missing
pixel-0509numeric256 unique values
0 missing
pixel-0765numeric256 unique values
0 missing
pixel-0254numeric256 unique values
0 missing
pixel-0510numeric256 unique values
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0 missing
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0 missing

62 properties

270912
Number of instances (rows) of the dataset.
785
Number of attributes (columns) of the dataset.
49
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.
1.01
First quartile of skewness among attributes of the numeric type.
45.7
Mean of means among attributes of the numeric type.
61.92
First quartile of standard deviation of attributes of the numeric type.
0.02
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
5.47
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.
0.88
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
49
Average number of distinct values among the attributes of the nominal type.
46.25
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.
2.62
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
2.58
Percentage of instances belonging to the most frequent class.
78.21
Mean standard deviation of attributes of the numeric type.
1.61
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.
87.25
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.64
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1043.64
Maximum kurtosis among attributes of the numeric type.
0.25
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
7.01
Third quartile of kurtosis among attributes of the numeric type.
102.45
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.
68.51
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
49
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.
49
The maximum number of distinct values among attributes of the nominal type.
0.37
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.91
Third quartile of skewness among attributes of the numeric type.
30.32
Maximum skewness among attributes of the numeric type.
5.82
Minimum standard deviation of attributes of the numeric type.
-0.74
First quartile of kurtosis among attributes of the numeric type.
99.62
Third quartile of standard deviation of attributes of the numeric type.
108.43
Maximum standard deviation of attributes of the numeric type.
0.17
Percentage of instances belonging to the least frequent class.
456
Number of instances belonging to the least frequent class.
21.74
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.
15.66
Mean kurtosis among attributes of the numeric type.

2 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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