Data
papir_2

papir_2

active ARFF Publicly available Visibility: public Uploaded 29-09-2014 by Joaquin Vanschoren
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
Author: Magne Aldrin (magne.aldrin@nr.no) Source: [StatLib](http://lib.stat.cmu.edu/datasets/) - April 14. 1999 Please cite: One of two multivariate regression data sets from paper industry, from an experiment at the paper plant Saugbruksforeningen, Norway. They have been described and analysed in: Aldrin, M. (1996), "Moderate projection pursuit regression for multivariate response data", Computational Statistics and Data Analysis, 21, p. 501-531. It consists of 30 observations (rows) and 41 variables (columns). Columns 1 to 32 are response variables that describes various qualities of the paper. Columns 33 to 41 are 9 predictor variables. The first three predictor variables (x1 in column 33, x2 in column 34 and x3 in column 35) were varied systematically through the experiment. The next three predictor variables (columns 36 to 38) are constructed as x12, x22 and x32. The last three predictor variables (columns 39 to 41) are constructed as x1*x2, x1*x3 and x2*x3.

41 features

response_1 (target)numeric19 unique values
0 missing
response_2numeric21 unique values
0 missing
response_3numeric18 unique values
0 missing
response_4numeric23 unique values
0 missing
response_5numeric20 unique values
0 missing
response_6numeric20 unique values
0 missing
response_7numeric21 unique values
0 missing
response_8numeric22 unique values
0 missing
response_9numeric25 unique values
0 missing
response_10numeric21 unique values
0 missing
response_11numeric24 unique values
0 missing
response_12numeric23 unique values
0 missing
response_13numeric26 unique values
0 missing
response_14numeric22 unique values
0 missing
response_15numeric23 unique values
0 missing
response_16numeric27 unique values
0 missing
response_17numeric19 unique values
0 missing
response_18numeric17 unique values
0 missing
response_19numeric19 unique values
0 missing
response_20numeric18 unique values
0 missing
response_21numeric18 unique values
0 missing
response_22numeric19 unique values
0 missing
response_23numeric19 unique values
0 missing
response_24numeric18 unique values
0 missing
response_25numeric22 unique values
0 missing
response_26numeric25 unique values
0 missing
response_27numeric23 unique values
0 missing
response_28numeric21 unique values
0 missing
response_29numeric21 unique values
0 missing
response_30numeric22 unique values
0 missing
response_31numeric24 unique values
0 missing
response_32numeric23 unique values
0 missing
x1numeric27 unique values
0 missing
x2numeric23 unique values
0 missing
x3numeric19 unique values
0 missing
x1**2numeric24 unique values
0 missing
x2**2numeric23 unique values
0 missing
x3**2numeric19 unique values
0 missing
x1*x2numeric30 unique values
0 missing
x2*x3numeric29 unique values
0 missing
x3*x2numeric29 unique values
0 missing

107 properties

30
Number of instances (rows) of the dataset.
41
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.
41
Number of numeric attributes.
0
Number of nominal attributes.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
1.02
Mean standard deviation of attributes of the numeric type.
-0.22
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
14.34
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.54
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4.57
Maximum kurtosis among attributes of the numeric type.
-0.01
Minimum of means among attributes of the numeric type.
-0.07
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
22.74
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
1.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1.37
Number of attributes divided by the number of instances.
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 instances having missing values.
Third quartile of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
The maximum number of distinct values among attributes of the nominal type.
-1.49
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
0.28
Third quartile of kurtosis among attributes of the numeric type.
-0.14
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.41
Maximum skewness among attributes of the numeric type.
0.94
Minimum standard deviation of attributes of the numeric type.
100
Percentage of numeric attributes.
16.67
Third quartile of means among attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.19
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
0.33
Third quartile of skewness among attributes of the numeric type.
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.53
First quartile of kurtosis among attributes of the numeric type.
1.02
Third quartile of standard deviation of attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
12.43
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
13.46
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
-0.23
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 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
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Average number of distinct values among the attributes of the nominal type.
1.02
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.03
Mean skewness among attributes of the numeric type.

5 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: response_1
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: response_1
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task