Data
Reading_Hydro_downstream

Reading_Hydro_downstream

active ARFF Publicly available Visibility: public Uploaded 30-09-2022 by Laurens Krudde
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Upstream data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK.

2 features

downstream (target)numeric55 unique values
0 missing
timestampnumeric1000 unique values
0 missing
Show all 2 features

19 properties

NumberOfInstances
1000
Number of instances (rows) of the dataset.
NumberOfFeatures
2
Number of attributes (columns) of the dataset.
NumberOfClasses
0
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
2
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
AutoCorrelation
1
Average class difference between consecutive instances.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
Show all 19 properties

2 tasks

Supervised Regression on Reading_Hydro_downstream
1 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: downstream
Supervised Regression on Reading_Hydro_downstream
1 runs - estimation_procedure: 10% Holdout set - evaluation_measure: root_mean_squared_error - target_feature: downstream
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