Data
airlines

airlines

active ARFF Publicly available Visibility: public Uploaded 10-10-2014 by Joaquin Vanschoren
0 likes downloaded by 26 people , 34 total downloads 0 issues 0 downvotes
  • concept_drift study_16 study_218 study_69
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Albert Bifet, Elena Ikonomovska Source: [Data Expo competition](http://kt.ijs.si/elena_ikonomovska/data.html) - 2009 Please cite: Airlines Dataset Inspired in the regression dataset from Elena Ikonomovska. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.

8 features

Delay (target)nominal2 unique values
0 missing
Airlinenominal18 unique values
0 missing
Flightnumeric6585 unique values
0 missing
AirportFromnominal293 unique values
0 missing
AirportTonominal293 unique values
0 missing
DayOfWeeknominal7 unique values
0 missing
Timenumeric1131 unique values
0 missing
Lengthnumeric426 unique values
0 missing

107 properties

539383
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
2
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.
3
Number of numeric attributes.
5
Number of nominal attributes.
0.87
Second quartile (Median) of skewness among attributes of the numeric type.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
2427.93
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
278.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.05
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
12.5
Percentage of binary attributes.
6.46
Third quartile of entropy among attributes.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
40.59
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
293
The maximum number of distinct values among attributes of the nominal type.
0.08
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2.76
Third quartile of kurtosis among attributes of the numeric type.
0.58
Average class difference between consecutive instances.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.49
Maximum skewness among attributes of the numeric type.
70.12
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
2427.93
Third quartile of means among attributes of the numeric type.
0.66
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2067.43
Maximum standard deviation of attributes of the numeric type.
44.54
Percentage of instances belonging to the least frequent class.
37.5
Percentage of numeric attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
0.36
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.89
Average entropy of the attributes.
240264
Number of instances belonging to the least frequent class.
62.5
Percentage of nominal attributes.
1.49
Third quartile of skewness among attributes of the numeric type.
0.25
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.46
Mean kurtosis among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.06
First quartile of entropy among attributes.
2067.43
Third quartile of standard deviation of attributes of the numeric type.
0.66
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1120.95
Mean of means among attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.01
First quartile of kurtosis among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.36
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Average mutual information between the nominal attributes and the target attribute.
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
132.2
First quartile of means among attributes of the numeric type.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
199.18
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
0.08
First quartile of skewness among attributes of the numeric type.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.66
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
155.66
Standard deviation of the number of distinct values among attributes of the nominal type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
122.6
Average number of distinct values among the attributes of the nominal type.
70.12
First quartile of standard deviation of attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.36
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.81
Mean skewness among attributes of the numeric type.
5.15
Second quartile (Median) of entropy among attributes.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.25
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
0.4
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.46
Percentage of instances belonging to the most frequent class.
805.2
Mean standard deviation of attributes of the numeric type.
-0.36
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Entropy of the target attribute values.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
299119
Number of instances belonging to the most frequent class.
2.79
Minimal entropy among attributes.
802.73
Second quartile (Median) of means among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
6.46
Maximum entropy among attributes.
-1.01
Minimum kurtosis among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
2.76
Maximum kurtosis among attributes of the numeric type.
132.2
Minimum of means among attributes of the numeric type.

7 tasks

0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Delay
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Delay
287 runs - estimation_procedure: Interleaved Test then Train - target_feature: Delay
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering - target_feature: clusters
Define a new task