Data
fars

fars

active ARFF public Visibility: public Uploaded 06-04-2017 by Pieter Gijsbers
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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

62 properties

100968
Number of instances (rows) of the dataset.
30
Number of attributes (columns) of the dataset.
8
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.
14
Number of numeric attributes.
16
Number of nominal attributes.
-1.21
First quartile of skewness among attributes of the numeric type.
46.56
Mean of means among attributes of the numeric type.
2
First quartile of standard deviation of attributes of the numeric type.
0.33
Average class difference between consecutive instances.
0.16
Average mutual information between the nominal attributes and the target attribute.
1.03
Second quartile (Median) of entropy among attributes.
2.18
Entropy of the target attribute values.
5.53
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2.52
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
6.31
Average number of distinct values among the attributes of the nominal type.
25.14
Second quartile (Median) of means among attributes of the numeric type.
13.88
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-0
Mean skewness among attributes of the numeric type.
0.07
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
41.71
Percentage of instances belonging to the most frequent class.
77.43
Mean standard deviation of attributes of the numeric type.
0.75
Second quartile (Median) of skewness among attributes of the numeric type.
42116
Number of instances belonging to the most frequent class.
0.42
Minimal entropy among attributes.
0
Percentage of binary attributes.
4.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.72
Maximum entropy among attributes.
-1.42
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
1.49
Third quartile of entropy among attributes.
201.94
Maximum kurtosis among attributes of the numeric type.
4.98
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
32
Third quartile of kurtosis among attributes of the numeric type.
207.39
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
46.67
Percentage of numeric attributes.
74.88
Third quartile of means among attributes of the numeric type.
0.98
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
53.33
Percentage of nominal attributes.
0.15
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.
-5.47
Minimum skewness among attributes of the numeric type.
0.64
First quartile of entropy among attributes.
1.57
Third quartile of skewness among attributes of the numeric type.
2.75
Maximum skewness among attributes of the numeric type.
0.81
Minimum standard deviation of attributes of the numeric type.
0.06
First quartile of kurtosis among attributes of the numeric type.
104.76
Third quartile of standard deviation of attributes of the numeric type.
396.19
Maximum standard deviation of attributes of the numeric type.
0.01
Percentage of instances belonging to the least frequent class.
9
Number of instances belonging to the least frequent class.
11.04
First quartile of means among attributes of the numeric type.
2.63
Standard deviation of the number of distinct values among attributes of the nominal type.
1.03
Average entropy of the attributes.
0
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
25.54
Mean kurtosis among attributes of the numeric type.

13 tasks

1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - 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
Define a new task