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LED-display-domain-7digit

LED-display-domain-7digit

active ARFF Publicly available Visibility: public Uploaded 29-07-2016 by Rafael G. Mantovani
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  • artificial keel OpenML100 study_123 study_135 study_14 study_50 study_52 study_76 study_88 uci
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Author: Breiman,L., Friedman,J.H., Olshen,R.A., and Stone,C.J. Source: [UCI](http://archive.ics.uci.edu/ml/datasets/LED+Display+Domain), [KEEL](http://sci2s.ugr.es/keel/dataset.php?cod=63, https://archive.ics.uci.edu/ml/datasets/LED+Display+Domain) - 1988 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) LED display data set This simple domain contains 7 Boolean attributes and 10 classes, the set of decimal digits. Recall that LED displays contain 7 light-emitting diodes -- hence the reason for 7 attributes. The class attribute is an integer ranging between 0 and 9 inclusive, representing the possible digits show on the display. The problem would be easy if not for the introduction of noise. In this case, each attribute value has the 10% probability of having its value inverted. It's valuable to know the optimal Bayes rate for these databases. In this case, the misclassification rate is 26% (74% classification accuracy). ### Attribute Information * V1-V7 represent each of the 7 LEDs, with values either 0 or 1, according to whether the corresponding light is on or not for the decimal digit. Each has a 10% percent chance of being inverted.

8 features

Class (target)nominal10 unique values
0 missing
V1numeric2 unique values
0 missing
V2numeric2 unique values
0 missing
V3numeric2 unique values
0 missing
V4numeric2 unique values
0 missing
V5numeric2 unique values
0 missing
V6numeric2 unique values
0 missing
V7numeric2 unique values
0 missing

62 properties

500
Number of instances (rows) of the dataset.
8
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.
7
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0.47
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.87
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.74
Maximum kurtosis among attributes of the numeric type.
0.4
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-0.87
Third quartile of kurtosis among attributes of the numeric type.
0.82
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
87.5
Percentage of numeric attributes.
0.73
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.
12.5
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.
-1.65
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
-0.37
Third quartile of skewness among attributes of the numeric type.
0.43
Maximum skewness among attributes of the numeric type.
0.39
Minimum standard deviation of attributes of the numeric type.
-1.83
First quartile of kurtosis among attributes of the numeric type.
0.49
Third quartile of standard deviation of attributes of the numeric type.
0.49
Maximum standard deviation of attributes of the numeric type.
7.4
Percentage of instances belonging to the least frequent class.
0.59
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.
37
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
-1.12
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
-1.06
First quartile of skewness among attributes of the numeric type.
0.66
Mean of means among attributes of the numeric type.
0.44
First quartile of standard deviation of attributes of the numeric type.
0.72
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
3.31
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.
-1.48
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
0.67
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.
-0.73
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
11.4
Percentage of instances belonging to the most frequent class.
0.46
Mean standard deviation of attributes of the numeric type.
-0.73
Second quartile (Median) of skewness among attributes of the numeric type.
57
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

7 tasks

9607 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
31 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-fold Crossvalidation - target_feature: Class
0 runs - target_feature: Class
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