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hutsof99_child_witness

hutsof99_child_witness

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Author: Source: Unknown - Date unknown Please cite: Graeme D. Hutcheson and Nick Sofroniou 1999 The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models. SAGE Publications. Copyright: Graeme D. Hutcheson & Nick Sofroniou, 1999 This software can be freely used for non-commercial purposes and can be freely distributed. Readme file =========== The data sets in this directory are taken from the above book. The data are presented in two formats, *.dat (ascii) and *.por (SPSS portable). The GLIM code and macros are provided in files *.glm and *.mac. Please read the errata file which indicates some minor differences between these data sets and those reported in the book. DATA FILE SOURCE IN BOOK DESCRIPTION Chapter 1 tab1_01.* Table 1.1 Video Games and Hostility Chapter 2 tab2_01.* Table 2.1 Normal Errors tab2_02.* Table 2.2 Skewed Errors tab2_03.* Table 2.3 Curvilinearity Chapter 3 tab3_01.* Table 3.1 Two Simple Models tab3_05.* Table 3.5 Cost and Sound Quality tab3_07.* Table 3.7 Exam marks and College Offers tab3_11.* Table 3.11 Quality of Children's Testimonies Age: 5-6 = 0; 8-9 = 1 Gender: female = 0; male = 1 Location: 1 = home; 2 = school; 3 = police interview 4 = special interview tab3_11d.* Table 3.11 Data in Table 3.11 with indicator dummy codes added Chapter 4 tab4_01.* Table 4.1 Infection Severity and Treatment Outcome Treatment Outcome: 0 = survived 1 = died tab4_14.* Table 4.14 Infection severity, Treatment outcome and Hospital Attended Hospital: 1 = hospital A 2 = hospital B 3 = hospital C tab4.14d.* Table 4.14 Infection severity, Treatment outcome and Hospital Attended including dummy codes logis.* Child witness data: copy of tab3_11, but includes prosecution logis_d.* Child witness data: copy of tab3_11d, but includes prosecution logis.por and logis_d.por provide the data to obtain the parameters calculated in the book (pages 147 to 152). It should be noted that these differ slightly to the parameters obtained using the data sets logis.dat and logis_d.dat, as the *.dat files only record the variable 'coherence' to 2 decimal places. Chapter 5 tab5_01.* Table 5.1 Job Satisfaction for doctors and dentists tab5_04.* Table 5.4 Race, Housing and Illness tab5_07.* Table 5.7 Dopamine and psychosis: integer scoring tab5_08.* Table 5.8 Dopamine and psychosis: mid-ranks scoring tab5_10.* Table 5.10 Treatment and Depression: integer scoring tab5_11.* Table 5.11 Treatment and depression: mid-ranks scoring tab5_13.* Table 5.13 Alcohol consumption and Libido: integer scores tab5_16.* Table 5.16 Alcohol consumption and libido: low vs medium or high tab5_17.* Table 5.17 Alcohol consumption and libido: medium vs high Chapter 6 tab6_11.* Table 6.11 Child witness example data set File: ../data/hutsof99/tab6_11.dat Note: changes from Errata.txt where not included! Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

17 features

acc (target)numeric39 unique values
0 missing
subj (row identifier)numeric42 unique values
0 missing
Q1numeric17 unique values
0 missing
Q2numeric22 unique values
0 missing
Q3numeric23 unique values
0 missing
Q4numeric21 unique values
0 missing
Q5numeric18 unique values
0 missing
Q6numeric18 unique values
0 missing
Q7numeric18 unique values
0 missing
Q8numeric15 unique values
0 missing
Q9numeric18 unique values
0 missing
Q10numeric17 unique values
0 missing
Q11numeric22 unique values
0 missing
Q12numeric21 unique values
0 missing
Q13numeric21 unique values
0 missing
Q14numeric21 unique values
0 missing
compnumeric39 unique values
0 missing

107 properties

42
Number of instances (rows) of the dataset.
17
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.
17
Number of numeric attributes.
0
Number 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.
0
Percentage of nominal attributes.
0.29
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
-0.57
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.89
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
9.53
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.86
First quartile of kurtosis 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
2.61
First quartile of means among attributes of the numeric type.
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.
First quartile of mutual information between the nominal attributes and the target attribute.
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.
-0.26
First quartile of skewness among 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
-0.01
Mean skewness among attributes of the numeric type.
0.69
First quartile of standard deviation of attributes of the numeric type.
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.
2.71
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
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.
-0.5
Second quartile (Median) of kurtosis 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.
-1.29
Minimum kurtosis among attributes of the numeric type.
3.11
Second quartile (Median) of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.09
Maximum kurtosis among attributes of the numeric type.
1.98
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
69.91
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
-0.08
Second quartile (Median) of skewness among attributes of the numeric type.
0.8
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
0.4
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 binary attributes.
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.
-0.42
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
-0.32
Third quartile of kurtosis among attributes of the numeric type.
-10.58
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
0.64
Maximum skewness among attributes of the numeric type.
0.63
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
3.42
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
20.66
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric attributes.

5 tasks

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