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USPS

USPS

active ARFF Public Domain (CC0) Visibility: public Uploaded 14-05-2018 by Hall
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The dataset and this description is made available on http://www-stat.stanford.edu/~tibs/ElemStatLearn/data.html. Normalized handwritten digits, automatically scanned from envelopes by the U.S. Postal Service. The original scanned digits are binary and of different sizes and orientations; the images here have been deslanted and size normalized, resulting in 16 x 16 grayscale images (Le Cun et al., 1990). The data are in two gzipped files, and each line consists of the digit id (0-9) followed by the 256 grayscale values. There are 7291 training observations and 2007 test observations, distributed as follows: 0 1 2 3 4 5 6 7 8 9 Total Train 1194 1005 731 658 652 556 664 645 542 644 7291 Test 359 264 198 166 200 160 170 147 166 177 2007 or as proportions: 0 1 2 3 4 5 6 7 8 9 Train 0.16 0.14 0.1 0.09 0.09 0.08 0.09 0.09 0.07 0.09 Test 0.18 0.13 0.1 0.08 0.10 0.08 0.08 0.07 0.08 0.09 Alternatively, the training data are available as separate files per digit (and hence without the digit identifier in each row) The test set is notoriously "difficult", and a 2.5% error rate is excellent. These data were kindly made available by the neural network group at AT&T research labs (thanks to Yann Le Cunn). PS: In this dataset, the class is represented as 1-10

257 features

int0 (target)nominal10 unique values
0 missing
double1numeric1617 unique values
0 missing
double2numeric3010 unique values
0 missing
double3numeric4733 unique values
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double4numeric6345 unique values
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double5numeric8037 unique values
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double6numeric8948 unique values
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double7numeric9092 unique values
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double9numeric9123 unique values
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double10numeric9122 unique values
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double11numeric9033 unique values
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double12numeric8260 unique values
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double13numeric6775 unique values
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double14numeric5243 unique values
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double15numeric3403 unique values
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double16numeric1931 unique values
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double17numeric2326 unique values
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double18numeric4144 unique values
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double19numeric5974 unique values
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double20numeric7236 unique values
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double21numeric8771 unique values
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double22numeric9198 unique values
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double23numeric9226 unique values
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double24numeric9219 unique values
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double25numeric9206 unique values
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double26numeric9206 unique values
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double27numeric9188 unique values
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double28numeric8795 unique values
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double29numeric7404 unique values
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double30numeric6143 unique values
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double31numeric4371 unique values
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double32numeric2528 unique values
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double33numeric3014 unique values
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double34numeric5064 unique values
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double35numeric6671 unique values
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double36numeric7797 unique values
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double37numeric9050 unique values
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double38numeric9259 unique values
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double39numeric9265 unique values
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double46numeric6617 unique values
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double131numeric6500 unique values
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double134numeric8806 unique values
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double135numeric8991 unique values
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double136numeric8854 unique values
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double137numeric8779 unique values
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double140numeric9076 unique values
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double144numeric3939 unique values
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double196numeric6789 unique values
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double212numeric6667 unique values
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double214numeric9123 unique values
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double215numeric9248 unique values
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double216numeric9254 unique values
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double217numeric9265 unique values
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double218numeric9270 unique values
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double219numeric9243 unique values
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double220numeric8825 unique values
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double221numeric6969 unique values
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double222numeric5836 unique values
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double223numeric4843 unique values
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double224numeric3554 unique values
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double225numeric3314 unique values
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double226numeric4718 unique values
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double227numeric5628 unique values
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double228numeric6485 unique values
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double230numeric9005 unique values
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double231numeric9194 unique values
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double232numeric9221 unique values
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double233numeric9224 unique values
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double234numeric9218 unique values
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double235numeric9150 unique values
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double236numeric8459 unique values
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double237numeric6566 unique values
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double238numeric5446 unique values
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double239numeric4205 unique values
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double240numeric2791 unique values
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double241numeric2288 unique values
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double242numeric3799 unique values
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double243numeric5140 unique values
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double244numeric6107 unique values
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double245numeric7825 unique values
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double246numeric8841 unique values
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double247numeric9076 unique values
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double248numeric9139 unique values
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double249numeric9142 unique values
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double250numeric9107 unique values
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double251numeric8923 unique values
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double252numeric7824 unique values
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double253numeric5988 unique values
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double254numeric4571 unique values
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double255numeric3120 unique values
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double256numeric1834 unique values
0 missing
Show first 100 features

62 properties

NumberOfInstances
9298
Number of instances (rows) of the dataset.
NumberOfFeatures
257
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
256
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
99.61
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.39
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.23
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0.8
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
0.28
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.39
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.69
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-0.47
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
0.81
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.56
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
3.94
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
-0.25
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
2.2
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.62
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.12
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
-0.5
Mean of means among attributes of the numeric type.
ClassEntropy
3.27
Entropy of the target attribute values.
Dimensionality
0.03
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
16.7
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1553
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
105.45
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.24
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
9.39
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.68
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
4.77
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
1.47
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.49
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.57
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.99
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
-0.95
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.05
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
7.61
Percentage of instances belonging to the least frequent class.
MinorityClassSize
708
Number of instances belonging to the least frequent class.
Show all 62 properties

11 tasks

Supervised Classification on USPS
57 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: int0
Supervised Classification on USPS
0 runs - estimation_procedure: 33% Holdout set - target_feature: int0
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Clustering on USPS
0 runs - estimation_procedure: 50 times Clustering
Define a new task