Data
ozone-level-8hr

ozone-level-8hr

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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  • OpenML-CC18 OpenML100 study_123 study_14 study_34 study_50 study_52 study_7 study_98 study_99
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Author: Kun Zhang, Wei Fan, XiaoJing Yuan Source: [UCI](https://archive.ics.uci.edu/ml/datasets/ozone+level+detection) Please cite: Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008. 1 . Abstract: Two ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area. 2. Source: Kun Zhang, zhang.kun05 '@' gmail.com, Department of Computer Science, Xavier University of Lousiana Wei Fan, wei.fan '@' gmail.com, IBM T.J.Watson Research XiaoJing Yuan, xyuan '@' uh.edu, Engineering Technology Department, College of Technology, University of Houston 3. Data Set Information: All the attribute start with T means the temperature measured at different time throughout the day; and those starts with WS indicate the wind speed at various time. WSR_PK: continuous. peek wind speed -- resultant (meaning average of wind vector) WSR_AV: continuous. average wind speed T_PK: continuous. Peak T T_AV: continuous. Average T T85: continuous. T at 850 hpa level (or about 1500 m height) RH85: continuous. Relative Humidity at 850 hpa U85: continuous. (U wind - east-west direction wind at 850 hpa) V85: continuous. V wind - N-S direction wind at 850 HT85: continuous. Geopotential height at 850 hpa, it is about the same as height at low altitude T70: continuous. T at 700 hpa level (roughly 3100 m height) RH70: continuous. U70: continuous. V70: continuous. HT70: continuous. T50: continuous. T at 500 hpa level (roughly at 5500 m height) RH50: continuous. U50: continuous. V50: continuous. HT50: continuous. KI: continuous. K-Index [Web Link] TT: continuous. T-Totals [Web Link] SLP: continuous. Sea level pressure SLP_: continuous. SLP change from previous day Precp: continuous. -- precipitation 4. Attribute Information: The following are specifications for several most important attributes that are highly valued by Texas Commission on Environmental Quality (TCEQ). More details can be found in the two relevant papers. O 3 - Local ozone peak prediction Upwind - Upwind ozone background level EmFactor - Precursor emissions related factor Tmax - Maximum temperature in degrees F Tb - Base temperature where net ozone production begins (50 F) SRd - Solar radiation total for the day WSa - Wind speed near sunrise (using 09-12 UTC forecast mode) WSp - Wind speed mid-day (using 15-21 UTC forecast mode) 5. Relevant Papers: Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008. It Discusses details about the dataset, its use as well as various experiments (both cross-validation and streaming) using many state-of-the-art methods. A shorter version of the paper (does not contain some detailed experiments as the journal paper above) is in: Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions. ICDM 2006: 753-764

73 features

Class (target)nominal2 unique values
0 missing
V1numeric69 unique values
0 missing
V2numeric71 unique values
0 missing
V3numeric66 unique values
0 missing
V4numeric67 unique values
0 missing
V5numeric65 unique values
0 missing
V6numeric64 unique values
0 missing
V7numeric67 unique values
0 missing
V8numeric68 unique values
0 missing
V9numeric70 unique values
0 missing
V10numeric71 unique values
0 missing
V11numeric77 unique values
0 missing
V12numeric78 unique values
0 missing
V13numeric78 unique values
0 missing
V14numeric79 unique values
0 missing
V15numeric78 unique values
0 missing
V16numeric79 unique values
0 missing
V17numeric73 unique values
0 missing
V18numeric74 unique values
0 missing
V19numeric71 unique values
0 missing
V20numeric66 unique values
0 missing
V21numeric69 unique values
0 missing
V22numeric70 unique values
0 missing
V23numeric69 unique values
0 missing
V24numeric66 unique values
0 missing
V25numeric75 unique values
0 missing
V26numeric56 unique values
0 missing
V27numeric283 unique values
0 missing
V28numeric285 unique values
0 missing
V29numeric288 unique values
0 missing
V30numeric284 unique values
0 missing
V31numeric284 unique values
0 missing
V32numeric292 unique values
0 missing
V33numeric296 unique values
0 missing
V34numeric312 unique values
0 missing
V35numeric314 unique values
0 missing
V36numeric315 unique values
0 missing
V37numeric328 unique values
0 missing
V38numeric331 unique values
0 missing
V39numeric335 unique values
0 missing
V40numeric336 unique values
0 missing
V41numeric336 unique values
0 missing
V42numeric340 unique values
0 missing
V43numeric338 unique values
0 missing
V44numeric330 unique values
0 missing
V45numeric322 unique values
0 missing
V46numeric307 unique values
0 missing
V47numeric303 unique values
0 missing
V48numeric295 unique values
0 missing
V49numeric288 unique values
0 missing
V50numeric285 unique values
0 missing
V51numeric331 unique values
0 missing
V52numeric297 unique values
0 missing
V53numeric252 unique values
0 missing
V54numeric101 unique values
0 missing
V55numeric1289 unique values
0 missing
V56numeric1462 unique values
0 missing
V57numeric369 unique values
0 missing
V58numeric246 unique values
0 missing
V59numeric101 unique values
0 missing
V60numeric1538 unique values
0 missing
V61numeric1430 unique values
0 missing
V62numeric442 unique values
0 missing
V63numeric187 unique values
0 missing
V64numeric101 unique values
0 missing
V65numeric1688 unique values
0 missing
V66numeric1510 unique values
0 missing
V67numeric86 unique values
0 missing
V68numeric1048 unique values
0 missing
V69numeric658 unique values
0 missing
V70numeric72 unique values
0 missing
V71numeric57 unique values
0 missing
V72numeric175 unique values
0 missing

62 properties

2534
Number of instances (rows) of the dataset.
73
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.
72
Number of numeric attributes.
1
Number of nominal attributes.
1.37
Percentage of binary attributes.
6.23
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.98
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
77.66
Maximum kurtosis among attributes of the numeric type.
-10.51
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
1.06
Third quartile of kurtosis among attributes of the numeric type.
10164.2
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
98.63
Percentage of numeric attributes.
20.78
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
1.37
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-1.31
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.51
Third quartile of skewness among attributes of the numeric type.
7.38
Maximum skewness among attributes of the numeric type.
0.24
Minimum standard deviation of attributes of the numeric type.
-0.26
First quartile of kurtosis among attributes of the numeric type.
7.04
Third quartile of standard deviation of attributes of the numeric type.
77.41
Maximum standard deviation of attributes of the numeric type.
6.31
Percentage of instances belonging to the least frequent class.
1.84
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.
160
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
1.49
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
-0.65
First quartile of skewness among attributes of the numeric type.
296.49
Mean of means among attributes of the numeric type.
1.17
First quartile of standard deviation of attributes of the numeric type.
0.92
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.34
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.
0.21
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.03
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
4.82
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.
0.09
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
93.69
Percentage of instances belonging to the most frequent class.
7.57
Mean standard deviation of attributes of the numeric type.
0.05
Second quartile (Median) of skewness among attributes of the numeric type.
2374
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

16 tasks

90654 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
85825 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: 33% Holdout set - target_feature: Class
45 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1310 runs - target_feature: Class
1302 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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