Data
fars

fars

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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Help us complete this description Edit
Re-upload of the dataset as it is present in the Penn ML Benchmark (https://github.com/EpistasisLab/penn-ml-benchmarks/tree/master/datasets/classification/fars). It's a dataset on traffic accidents, see https://data.world/nhtsa/fars-data. I am not sure of the specific date or aggregation method as it is just a re-upload.

30 features

class (target)nominal8 unique values
0 missing
CASE_STATEnumeric51 unique values
0 missing
AGEnumeric99 unique values
0 missing
SEXnominal3 unique values
0 missing
PERSON_TYPEnominal10 unique values
0 missing
SEATING_POSITIONnumeric26 unique values
0 missing
RESTRAINT_SYSTEM-USEnumeric12 unique values
0 missing
AIR_BAG_AVAILABILITY/DEPLOYMENTnumeric13 unique values
0 missing
EJECTIONnominal4 unique values
0 missing
EJECTION_PATHnominal10 unique values
0 missing
EXTRICATIONnominal3 unique values
0 missing
NON_MOTORIST_LOCATIONnumeric18 unique values
0 missing
POLICE_REPORTED_ALCOHOL_INVOLVEMENTnominal4 unique values
0 missing
METHOD_ALCOHOL_DETERMINATIONnominal7 unique values
0 missing
ALCOHOL_TEST_TYPEnominal10 unique values
0 missing
ALCOHOL_TEST_RESULTnumeric69 unique values
0 missing
POLICE-REPORTED_DRUG_INVOLVEMENTnominal4 unique values
0 missing
METHOD_OF_DRUG_DETERMINATIONnominal5 unique values
0 missing
DRUG_TEST_TYPEnominal7 unique values
0 missing
DRUG_TEST_RESULTS_(1_of_3)numeric95 unique values
0 missing
DRUG_TEST_TYPE_(2_of_3)nominal7 unique values
0 missing
DRUG_TEST_RESULTS_(2_of_3)numeric73 unique values
0 missing
DRUG_TEST_TYPE_(3_of_3)nominal7 unique values
0 missing
DRUG_TEST_RESULTS_(3_of_3)numeric59 unique values
0 missing
HISPANIC_ORIGINnominal9 unique values
0 missing
TAKEN_TO_HOSPITALnominal3 unique values
0 missing
RELATED_FACTOR_(1)-PERSON_LEVELnumeric45 unique values
0 missing
RELATED_FACTOR_(2)-PERSON_LEVELnumeric48 unique values
0 missing
RELATED_FACTOR_(3)-PERSON_LEVELnumeric33 unique values
0 missing
RACEnumeric18 unique values
0 missing
Show all 30 features

62 properties

NumberOfInstances
100968
Number of instances (rows) of the dataset.
NumberOfFeatures
30
Number of attributes (columns) of the dataset.
NumberOfClasses
8
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
14
Number of numeric attributes.
NumberOfSymbolicFeatures
16
Number of nominal attributes.
MaxSkewnessOfNumericAtts
2.75
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.81
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
0.64
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
1.57
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
396.19
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.01
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
0.06
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
104.76
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.03
Average entropy of the attributes.
MinorityClassSize
9
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
11.04
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
2.63
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanKurtosisOfNumericAtts
25.54
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1MutualInformation
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
46.56
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
-1.21
First quartile of skewness among attributes of the numeric type.
MeanMutualInformation
0.16
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
2
First quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.33
Average class difference between consecutive instances.
MeanNoiseToSignalRatio
5.53
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
1.03
Second quartile (Median) of entropy among attributes.
ClassEntropy
2.18
Entropy of the target attribute values.
MeanNominalAttDistinctValues
6.31
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
2.52
Second quartile (Median) of kurtosis among attributes of the numeric type.
Dimensionality
0
Number of attributes divided by the number of instances.
MeanSkewnessOfNumericAtts
-0
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
25.14
Second quartile (Median) of means among attributes of the numeric type.
EquivalentNumberOfAtts
13.88
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
41.71
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
77.43
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
0.07
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassSize
42116
Number of instances belonging to the most frequent class.
MinAttributeEntropy
0.42
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
0.75
Second quartile (Median) of skewness among attributes of the numeric type.
MaxAttributeEntropy
1.72
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.42
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
4.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
201.94
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
4.98
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.49
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
207.39
Maximum of means among attributes of the numeric type.
MinMutualInformation
0.01
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
32
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
0.98
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
46.67
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
74.88
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-5.47
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
53.33
Percentage of nominal attributes.
Quartile3MutualInformation
0.15
Third quartile of mutual information between the nominal attributes and the target attribute.
Show all 62 properties

24 tasks

Supervised Classification on fars
1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
Supervised Classification on fars
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on fars
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on fars
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on fars
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on fars
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Clustering on fars
0 runs - estimation_procedure: 50 times Clustering
Define a new task