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mnist_784

mnist_784

active ARFF Publicly available Visibility: public Uploaded 29-09-2014 by Felicia West
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  • AzurePilot OpenML-CC18 OpenML100 study_1 study_123 study_41 study_99 vision study_14 study_225 study_293
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Author: Yann LeCun, Corinna Cortes, Christopher J.C. Burges Source: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown Please cite: The MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. With some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets. The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.

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Show first 100 features

107 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.
AutoCorrelation
0.1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
3.32
Entropy of the target attribute values.
DecisionStumpAUC
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.8
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
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.
J48.00001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
11.25
Percentage of instances belonging to the most frequent class.
MajorityClassSize
7877
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
70000
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
140.02
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
264.58
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
113.89
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
4894.71
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
33.39
Mean of means among attributes of the numeric type.
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
31.08
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
49.26
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.83
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
-0.24
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.02
Percentage of instances belonging to the least frequent class.
MinorityClassSize
6313
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
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
-1.03
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.12
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.87
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
4.67
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
16.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
7.2
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
4.13
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
36.77
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
623.08
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
69.33
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
23.94
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
100.85
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

33 tasks

Supervised Classification on mnist_784
13166 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on mnist_784
84 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mnist_784
60 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on mnist_784
2 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: 10% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: Custom Holdout - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: Custom Holdout - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: Custom Holdout - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mnist_784
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mnist_784
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on mnist_784
5 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on mnist_784
0 runs - target_feature: class
Clustering on mnist_784
0 runs
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
Clustering on mnist_784
0 runs - estimation_procedure: 50 times Clustering
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