OpenML
calendarDOW

calendarDOW

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Machine Learning study_88 Transportation
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
calendarDOW-pmlb

33 features

class (target)nominal5 unique values
0 missing
Feature00numeric24 unique values
0 missing
Feature01numeric32 unique values
0 missing
Feature02nominal10 unique values
0 missing
Feature03nominal8 unique values
0 missing
Feature04numeric13 unique values
0 missing
Feature05nominal8 unique values
0 missing
Feature06numeric16 unique values
0 missing
Feature07numeric18 unique values
0 missing
Feature08nominal9 unique values
0 missing
Feature09nominal6 unique values
0 missing
Feature10nominal6 unique values
0 missing
Feature11nominal6 unique values
0 missing
Feature12numeric46 unique values
0 missing
Feature13numeric70 unique values
0 missing
Feature14nominal7 unique values
0 missing
Feature15nominal7 unique values
0 missing
Feature16nominal7 unique values
0 missing
Feature17nominal7 unique values
0 missing
Feature18numeric51 unique values
0 missing
Feature19numeric90 unique values
0 missing
Feature20nominal4 unique values
0 missing
Feature21numeric14 unique values
0 missing
Feature22nominal3 unique values
0 missing
Feature23numeric21 unique values
0 missing
Feature24nominal2 unique values
0 missing
Feature25nominal2 unique values
0 missing
Feature26nominal3 unique values
0 missing
Feature27nominal3 unique values
0 missing
Feature28nominal9 unique values
0 missing
Feature29nominal9 unique values
0 missing
Feature30numeric15 unique values
0 missing
Feature31nominal3 unique values
0 missing

62 properties

399
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
5
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.
12
Number of numeric attributes.
21
Number of nominal attributes.
2.28
Entropy of the target attribute values.
7.94
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.91
Second quartile (Median) of entropy among attributes.
0.08
Number of attributes divided by the number of instances.
5.9
Average number of distinct values among the attributes of the nominal type.
1.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
11.58
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-1.94
Mean skewness among attributes of the numeric type.
18.52
Second quartile (Median) of means among attributes of the numeric type.
24.06
Percentage of instances belonging to the most frequent class.
9.07
Mean standard deviation of attributes of the numeric type.
0.17
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
96
Number of instances belonging to the most frequent class.
0.47
Minimal entropy among attributes.
-1.53
Second quartile (Median) of skewness among attributes of the numeric type.
2.69
Maximum entropy among attributes.
-1.18
Minimum kurtosis among attributes of the numeric type.
6.06
Percentage of binary attributes.
5.33
Second quartile (Median) of standard deviation of attributes of the numeric type.
14.11
Maximum kurtosis among attributes of the numeric type.
9.49
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
2.1
Third quartile of entropy among attributes.
70.79
Maximum of means among attributes of the numeric type.
0.03
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
10.34
Third quartile of kurtosis among attributes of the numeric type.
0.64
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
36.36
Percentage of numeric attributes.
40.24
Third quartile of means among attributes of the numeric type.
10
The maximum number of distinct values among attributes of the nominal type.
-3.63
Minimum skewness among attributes of the numeric type.
63.64
Percentage of nominal attributes.
0.26
Third quartile of mutual information between the nominal attributes and the target attribute.
-0.6
Maximum skewness among attributes of the numeric type.
1.92
Minimum standard deviation of attributes of the numeric type.
1.38
First quartile of entropy among attributes.
-1.19
Third quartile of skewness among attributes of the numeric type.
24.38
Maximum standard deviation of attributes of the numeric type.
11.03
Percentage of instances belonging to the least frequent class.
0.09
First quartile of kurtosis among attributes of the numeric type.
14.73
Third quartile of standard deviation of attributes of the numeric type.
1.76
Average entropy of the attributes.
44
Number of instances belonging to the least frequent class.
13.02
First quartile of means among attributes of the numeric type.
2.53
Standard deviation of the number of distinct values among attributes of the nominal type.
4.31
Mean kurtosis among attributes of the numeric type.
2
Number of binary attributes.
0.07
First quartile of mutual information between the nominal attributes and the target attribute.
26.32
Mean of means among attributes of the numeric type.
-3.05
First quartile of skewness among attributes of the numeric type.
0.39
Average class difference between consecutive instances.
0.2
Average mutual information between the nominal attributes and the target attribute.
2.9
First quartile of standard deviation of attributes of the numeric type.

21 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - 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: 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
0 runs - estimation_procedure: 50 times Clustering
Define a new task