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Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Andromeda dataset (Hatzikos et al. 2008) concerns the prediction of future values for six water quality variables (temperature, pH, conductivity, salinity, oxygen, turbidity) in Thermaikos Gulf of Thessaloniki, Greece. Measurements of the target variables are taken from under-water sensors with a sampling interval of 9 seconds and then averaged to get a single measurement for each variable over each day. The specific dataset that we use here corresponds to using a window of 5 days (i.e. features attributes correspond to the values of the six water quality variables up to 5 days in the past) and a lead of 5 days (i.e. we predict the values of each variable 6 days ahead).

36 features

Target (target)numeric47 unique values
0 missing
Target_2 (target)numeric23 unique values
0 missing
Target_3 (target)numeric30 unique values
0 missing
Target_4 (target)numeric37 unique values
0 missing
Target_5 (target)numeric47 unique values
0 missing
Target_6 (target)numeric48 unique values
0 missing
Window0_Att0numeric48 unique values
0 missing
Window0_Att1numeric25 unique values
0 missing
Window0_Att2numeric21 unique values
0 missing
Window0_Att3numeric32 unique values
0 missing
Window0_Att4numeric47 unique values
0 missing
Window0_Att5numeric46 unique values
0 missing
Window1_Att0numeric48 unique values
0 missing
Window1_Att1numeric25 unique values
0 missing
Window1_Att2numeric22 unique values
0 missing
Window1_Att3numeric33 unique values
0 missing
Window1_Att4numeric47 unique values
0 missing
Window1_Att5numeric46 unique values
0 missing
Window2_Att0numeric48 unique values
0 missing
Window2_Att1numeric24 unique values
0 missing
Window2_Att2numeric22 unique values
0 missing
Window2_Att3numeric33 unique values
0 missing
Window2_Att4numeric47 unique values
0 missing
Window2_Att5numeric46 unique values
0 missing
Window3_Att0numeric48 unique values
0 missing
Window3_Att1numeric24 unique values
0 missing
Window3_Att2numeric23 unique values
0 missing
Window3_Att3numeric33 unique values
0 missing
Window3_Att4numeric47 unique values
0 missing
Window3_Att5numeric46 unique values
0 missing
Window4_Att0numeric48 unique values
0 missing
Window4_Att1numeric24 unique values
0 missing
Window4_Att2numeric24 unique values
0 missing
Window4_Att3numeric34 unique values
0 missing
Window4_Att4numeric47 unique values
0 missing
Window4_Att5numeric46 unique values
0 missing

62 properties

49
Number of instances (rows) of the dataset.
36
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.
36
Number of numeric attributes.
0
Number of nominal attributes.
47.01
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.
100
Percentage of numeric 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.
-1.67
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
-0.04
Third quartile of skewness among attributes of the numeric type.
0.2
Maximum skewness among attributes of the numeric type.
0.59
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
3.73
Third quartile of standard deviation of attributes of the numeric type.
29.24
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-1.02
First quartile of kurtosis 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.
Number of instances belonging to the least frequent class.
6.12
First quartile of means among attributes of the numeric type.
-0.56
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
30.97
Mean of means among attributes of the numeric type.
-0.56
First quartile of skewness among attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
1.53
First quartile of standard deviation of attributes of the numeric type.
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.
Second quartile (Median) of entropy among attributes.
0.73
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
-0.41
Second quartile (Median) of kurtosis 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.
-0.32
Mean skewness among attributes of the numeric type.
26.07
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
6.03
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.29
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.84
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
2.21
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.85
Maximum kurtosis among attributes of the numeric type.
5.23
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
-0.19
Third quartile of kurtosis among attributes of the numeric type.
79.43
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

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