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yacht_hydrodynamics

yacht_hydrodynamics

active ARFF Public Domain (CC0) Visibility: public Uploaded 19-04-2020 by Rafael Gomes Mantovani
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Author: Ship Hydromechanics Laboratory","Maritime and Transport Technology Department","Technical University of Delft. Source: UCI - [original](http://archive.ics.uci.edu/ml/datasets/yacht+hydrodynamics) - Date unknown Please cite: Yacht Hydrodynamics Dataset Data Set Information Prediction of residuary resistance of sailing yachts at the initial design stage is of a great value for evaluating the ship’s performance and for estimating the required propulsive power. Essential inputs include the basic hull dimensions and the boat velocity. The Delft data set comprises 308 full-scale experiments, which were performed at the Delft Ship Hydromechanics Laboratory for that purpose. These experiments include 22 different hull forms, derived from a parent form closely related to the ‘Standfast 43’ designed by Frans Maas. Attribute Information Variations concern hull geometry coefficients and the Froude number: 1. Longitudinal position of the center of buoyancy, adimensional. 2. Prismatic coefficient, adimensional. 3. Length-displacement ratio, adimensional. 4. Beam-draught ratio, adimensional. 5. Length-beam ratio, adimensional. 6. Froude number, adimensional. The measured variable is the residuary resistance per unit weight of displacement: 7. Residuary resistance per unit weight of displacement, adimensional.

7 features

Residuary.resistance (target)numeric258 unique values
0 missing
Logitudinal.positionnumeric5 unique values
0 missing
Prismatic.coefficientnumeric10 unique values
0 missing
Length.displacement.rationumeric8 unique values
0 missing
Beam.draught.rationumeric17 unique values
0 missing
Length.beam.rationumeric10 unique values
0 missing
Froude.numbernumeric14 unique values
0 missing

19 properties

308
Number of instances (rows) of the dataset.
7
Number of attributes (columns) of the dataset.
0
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.
7
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of instances having missing values.
-6.3
Average class difference between consecutive instances.
0
Percentage of missing values.
0.02
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.

8 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: Residuary.resistance
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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