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ML2017-challenge-3

ML2017-challenge-3

in_preparation ARFF Publicly available Visibility: public Uploaded 09-06-2017 by Jan van Rijn
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Machine Learning Summer Course Data Mining Challenge

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pixel475numeric85 unique values
0 missing
pixel731numeric1 unique values
0 missing
pixel220numeric256 unique values
0 missing
pixel476numeric12 unique values
0 missing
pixel732numeric2 unique values
0 missing
pixel221numeric256 unique values
0 missing
pixel477numeric1 unique values
0 missing
pixel733numeric27 unique values
0 missing
pixel222numeric249 unique values
0 missing
pixel478numeric8 unique values
0 missing
pixel734numeric84 unique values
0 missing
pixel223numeric164 unique values
0 missing
pixel479numeric55 unique values
0 missing
pixel735numeric152 unique values
0 missing
pixel224numeric24 unique values
0 missing
pixel480numeric201 unique values
0 missing
pixel736numeric206 unique values
0 missing
pixel225numeric3 unique values
0 missing
pixel481numeric256 unique values
0 missing
pixel737numeric246 unique values
0 missing
pixel226numeric32 unique values
0 missing
pixel482numeric256 unique values
0 missing
pixel738numeric250 unique values
0 missing
pixel227numeric129 unique values
0 missing
pixel483numeric256 unique values
0 missing
pixel739numeric256 unique values
0 missing
pixel228numeric220 unique values
0 missing
pixel484numeric256 unique values
0 missing
pixel740numeric256 unique values
0 missing
pixel229numeric251 unique values
0 missing
pixel485numeric256 unique values
0 missing
pixel741numeric255 unique values
0 missing
pixel230numeric255 unique values
0 missing
pixel486numeric256 unique values
0 missing
pixel742numeric255 unique values
0 missing
pixel231numeric256 unique values
0 missing
pixel487numeric256 unique values
0 missing
pixel743numeric255 unique values
0 missing
pixel232numeric256 unique values
0 missing
pixel488numeric256 unique values
0 missing
pixel744numeric254 unique values
0 missing
pixel233numeric256 unique values
0 missing
pixel489numeric256 unique values
0 missing
pixel745numeric253 unique values
0 missing
pixel234numeric256 unique values
0 missing
pixel490numeric256 unique values
0 missing
pixel746numeric246 unique values
0 missing
pixel235numeric256 unique values
0 missing
pixel491numeric256 unique values
0 missing
pixel747numeric237 unique values
0 missing
pixel236numeric256 unique values
0 missing
pixel492numeric256 unique values
0 missing
pixel748numeric191 unique values
0 missing
pixel237numeric256 unique values
0 missing
pixel493numeric256 unique values
0 missing
pixel749numeric148 unique values
0 missing
pixel238numeric256 unique values
0 missing
pixel494numeric256 unique values
0 missing
pixel750numeric101 unique values
0 missing
pixel239numeric256 unique values
0 missing
pixel495numeric256 unique values
0 missing
pixel751numeric50 unique values
0 missing
pixel240numeric256 unique values
0 missing
pixel496numeric256 unique values
0 missing
pixel752numeric18 unique values
0 missing
pixel241numeric256 unique values
0 missing
pixel497numeric256 unique values
0 missing
pixel753numeric5 unique values
0 missing
pixel242numeric256 unique values
0 missing
pixel498numeric256 unique values
0 missing
pixel754numeric2 unique values
0 missing
pixel243numeric256 unique values
0 missing
pixel499numeric256 unique values
0 missing
pixel755numeric1 unique values
0 missing
pixel244numeric256 unique values
0 missing
pixel500numeric256 unique values
0 missing
pixel756numeric1 unique values
0 missing
pixel245numeric256 unique values
0 missing
pixel501numeric255 unique values
0 missing
pixel757numeric1 unique values
0 missing
pixel246numeric256 unique values
0 missing
pixel502numeric224 unique values
0 missing
pixel758numeric1 unique values
0 missing
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0 missing
pixel503numeric91 unique values
0 missing
pixel759numeric1 unique values
0 missing
pixel248numeric256 unique values
0 missing
pixel504numeric15 unique values
0 missing
pixel760numeric1 unique values
0 missing
pixel249numeric256 unique values
0 missing
pixel505numeric4 unique values
0 missing
pixel761numeric4 unique values
0 missing
pixel250numeric251 unique values
0 missing
pixel506numeric8 unique values
0 missing
pixel762numeric7 unique values
0 missing
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0 missing
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0 missing
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0 missing
pixel252numeric20 unique values
0 missing
pixel508numeric229 unique values
0 missing
pixel764numeric41 unique values
0 missing
pixel253numeric6 unique values
0 missing
pixel509numeric256 unique values
0 missing
pixel765numeric53 unique values
0 missing
pixel254numeric49 unique values
0 missing
pixel510numeric256 unique values
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.
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.
Second quartile (Median) of entropy among attributes.
0.01
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
16.23
Second quartile (Median) of kurtosis 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.
31.08
Mean skewness among attributes of the numeric type.
7.2
Second quartile (Median) of means among attributes of the numeric type.
11.25
Percentage of instances belonging to the most frequent class.
49.26
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
7877
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
4.13
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.83
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
36.77
Second quartile (Median) of standard deviation of attributes of the numeric type.
70000
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
140.02
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
623.08
Third quartile of kurtosis 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.
99.87
Percentage of numeric attributes.
69.33
Third quartile of means among attributes of the numeric type.
10
The maximum number of distinct values among attributes of the nominal type.
-0.24
Minimum skewness among attributes of the numeric type.
0.13
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
264.58
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
23.94
Third quartile of skewness among attributes of the numeric type.
113.89
Maximum standard deviation of attributes of the numeric type.
9.02
Percentage of instances belonging to the least frequent class.
-1.03
First quartile of kurtosis among attributes of the numeric type.
100.85
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
6313
Number of instances belonging to the least frequent class.
0.12
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.
4894.71
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
33.39
Mean of means among attributes of the numeric type.
0.87
First quartile of skewness among attributes of the numeric type.
0.1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
4.67
First quartile of standard deviation of attributes of the numeric type.

19 tasks

23 runs - estimation_procedure: 10-fold Crossvalidation - 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: 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
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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