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puma32H

puma32H

active ARFF Publicly available Visibility: public Uploaded 22-08-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Please cite: This is one of a family of datasets synthetically generated from a realistic simulation of the dynamics of a Unimation Puma 560 robot arm. There are eight datastets in this family . In this repository we only have two of them. They are all variations on the same model; a realistic simulation of the dynamics of a Puma 560 robot arm. The task in these datasets is to predict the angular accelaration of one of the robot arm's links. The inputs include angular positions, velocities and torques of the robot arm. The family has been specifically generated for the delve environment and so the individual datasets span the corners of a cube whose dimensions represent: Number of inputs 32 degree of non-linearity (fairly linear or non-linear) amount of noise in the output (moderate or high). Source: collection of regression datasets by Luis Torgo (torgo@ncc.up.pt) at http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html Original Source: DELVE repository of data. Characteristics: 8192 (4500+3692) cases, 33 continuous variables.

33 features

thetadd6 (target)numeric7861 unique values
0 missing
theta1numeric8188 unique values
0 missing
theta2numeric8186 unique values
0 missing
theta3numeric8186 unique values
0 missing
theta4numeric8185 unique values
0 missing
theta5numeric8178 unique values
0 missing
theta6numeric8180 unique values
0 missing
thetad1numeric8184 unique values
0 missing
thetad2numeric8187 unique values
0 missing
thetad3numeric8186 unique values
0 missing
thetad4numeric8183 unique values
0 missing
thetad5numeric8183 unique values
0 missing
thetad6numeric8188 unique values
0 missing
tau1numeric8192 unique values
0 missing
tau2numeric8192 unique values
0 missing
tau3numeric8192 unique values
0 missing
tau4numeric8192 unique values
0 missing
tau5numeric8192 unique values
0 missing
dm1numeric8181 unique values
0 missing
dm2numeric8180 unique values
0 missing
dm3numeric8178 unique values
0 missing
dm4numeric8187 unique values
0 missing
dm5numeric8173 unique values
0 missing
da1numeric8176 unique values
0 missing
da2numeric8173 unique values
0 missing
da3numeric8179 unique values
0 missing
da4numeric8179 unique values
0 missing
da5numeric8181 unique values
0 missing
db1numeric8180 unique values
0 missing
db2numeric8175 unique values
0 missing
db3numeric8176 unique values
0 missing
db4numeric8176 unique values
0 missing
db5numeric8173 unique values
0 missing

108 properties

8192
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
-1
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.
33
Number of numeric attributes.
0
Number of nominal attributes.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.83
First quartile of kurtosis among attributes of the numeric type.
0.51
DataQuality extracted from Fantail Library
-1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.09
Mean of means among attributes of the numeric type.
-1
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-0.12
First quartile of means among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1
Average mutual information between the nominal attributes and the target attribute.
0
Number of binary attributes.
-1
First quartile of mutual information between the nominal attributes and the target attribute.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
-1
Average number of distinct values among the attributes of the nominal type.
-0.58
First quartile of skewness among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
-1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Standard deviation of the number of distinct values among attributes of the nominal type.
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.42
Mean skewness among attributes of the numeric type.
0.34
DataQuality extracted from Fantail Library
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.77
Mean standard deviation of attributes of the numeric type.
-1
Second quartile (Median) of entropy among attributes.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
Entropy of the target attribute values.
-1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
-1
Percentage of instances belonging to the most frequent class.
-1
Minimal entropy among attributes.
-0.38
Second quartile (Median) of kurtosis among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
-1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
-1
Number of instances belonging to the most frequent class.
-1.47
Minimum kurtosis among attributes of the numeric type.
0.21
Second quartile (Median) of means among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-1
Maximum entropy among attributes.
-0.99
Minimum of means among attributes of the numeric type.
-1
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
-1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
136.43
Maximum kurtosis among attributes of the numeric type.
-1
Minimal mutual information between the nominal attributes and the target attribute.
-0.03
Second quartile (Median) of skewness among attributes of the numeric type.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
-1
The predictive accuracy obtained by always predicting the majority class.
1.39
Maximum of means among attributes of the numeric type.
-1
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.41
DataQuality extracted from Fantail Library
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
-1
Maximum mutual information between the nominal attributes and the target attribute.
-2.37
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
-1
Third quartile of entropy among attributes.
1
Average class difference between consecutive instances.
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
-1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-1
The maximum number of distinct values among attributes of the nominal type.
0.05
DataQuality extracted from Fantail Library
0
Percentage of missing values.
0.33
Third quartile of kurtosis among attributes of the numeric type.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.77
Maximum skewness among attributes of the numeric type.
0
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric attributes.
0.42
Third quartile of means among attributes of the numeric type.
-1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
43.75
DataQuality extracted from Fantail Library
-1
Number of instances belonging to the least frequent class.
0
Percentage of nominal attributes.
-1
Third quartile of mutual information between the nominal attributes and the target attribute.
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-1
Average entropy of the attributes.
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1
First quartile of entropy among attributes.
0.46
DataQuality extracted from Fantail Library
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
-1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.37
Mean kurtosis among attributes of the numeric type.

3 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: thetadd6
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: thetadd6
0 runs - estimation_procedure: 50 times Clustering
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