Data
jura

jura

active ARFF Publicly available Visibility: public Uploaded 14-03-2019 by Quay Au
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Jura (Goovaerts 1997) dataset consists of measurements of concentrations of seven heavy metals (cadmium, cobalt, chromium, copper, nickel, lead, and zinc), recorded at 359 locations in the topsoil of a region of the Swiss Jura. The type of land use (Forest, Pasture, Meadow, Tillage) and rock type (Argovian, Kimmeridgian, Sequanian, Portlandian, Quaternary) were also recorded for each location. In a typical scenario (Goovaerts 1997; Alvarez and Lawrence 2011), we are interested in the prediction of the concentration of metals that are more expensive to measure (primary variables) using measurements of metals that are cheaper to sample (secondary variables). In this study, cadmium, copper and lead are treated as target variables while the remaining metals along with land use type, rock type and the coordinates of each location are used as predictive features.

18 features

Cd (target)numeric276 unique values
0 missing
Co (target)numeric219 unique values
0 missing
Cu (target)numeric302 unique values
0 missing
Xlocnumeric341 unique values
0 missing
Ylocnumeric347 unique values
0 missing
Landuse_1numeric2 unique values
0 missing
Landuse_2numeric2 unique values
0 missing
Landuse_3numeric2 unique values
0 missing
Landuse_4numeric2 unique values
0 missing
Rock_1numeric2 unique values
0 missing
Rock_2numeric2 unique values
0 missing
Rock_3numeric2 unique values
0 missing
Rock_4numeric2 unique values
0 missing
Rock_5numeric2 unique values
0 missing
Crnumeric265 unique values
0 missing
Ninumeric277 unique values
0 missing
Pbnumeric254 unique values
0 missing
Znnumeric242 unique values
0 missing

62 properties

359
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
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.
18
Number of numeric attributes.
0
Number of nominal attributes.
0.25
First quartile of skewness among attributes of the numeric type.
12.64
Mean of means among attributes of the numeric type.
0.4
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
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.
0.27
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
0.95
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.
1.76
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
6.38
Mean standard deviation of attributes of the numeric type.
1.36
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.67
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.82
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
55.64
Maximum kurtosis among attributes of the numeric type.
0.02
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
6.56
Third quartile of kurtosis among attributes of the numeric type.
75.88
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100
Percentage of numeric attributes.
20.91
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.44
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.3
Third quartile of skewness among attributes of the numeric type.
7.57
Maximum skewness among attributes of the numeric type.
0.13
Minimum standard deviation of attributes of the numeric type.
-0.65
First quartile of kurtosis among attributes of the numeric type.
8.74
Third quartile of standard deviation of attributes of the numeric type.
33.1
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0.2
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
7.13
Mean kurtosis among attributes of the numeric type.

1 tasks

0 runs - estimation_procedure: 50 times Clustering
Define a new task