Data
sleep

sleep

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: Data from StatLib (ftp stat.cmu.edu/datasets) Data from which conclusions were drawn in the article "Sleep in Mammals: Ecological and Constitutional Correlates" by Allison, T. and Cicchetti, D. (1976), _Science_, November 12, vol. 194, pp. 732-734. Includes brain and body weight, life span, gestation time, time sleeping, and predation and danger indices for 62 mammals. Variables below (from left to right) for Mammals Data Set: species of animal body weight in kg brain weight in g slow wave ("nondreaming") sleep (hrs/day) paradoxical ("dreaming") sleep (hrs/day) total sleep (hrs/day) (sum of slow wave and paradoxical sleep) maximum life span (years) gestation time (days) predation index (1-5) 1 = minimum (least likely to be preyed upon) 5 = maximum (most likely to be preyed upon) sleep exposure index (1-5) 1 = least exposed (e.g. animal sleeps in a well-protected den) 5 = most exposed overall danger index (1-5) (based on the above two indices and other information) 1 = least danger (from other animals) 5 = most danger (from other animals) Note: Missing values denoted by -999.0 For more details, see Allison, Truett and Cicchetti, Domenic V. (1976), "Sleep in Mammals: Ecological and Constitutional Correlates", _Science_, November 12, vol. 194, pp. 732-734. The above data set can be freely used for non-commercial purposes and can be freely distributed (permission in writing obtained from Dr. Truett Allison). Submitted by Roger Johnson rwjohnso@silver.sdsmt.edu Total sleep treated as the class attribute. Attributes for slow wave and paradoxical sleep have been deleted. (The animal's name has also been deleted.)

8 features

danger_index (target)numeric5 unique values
0 missing
body_weightnumeric60 unique values
0 missing
brain_weightnumeric59 unique values
0 missing
max_life_spannumeric47 unique values
4 missing
gestation_timenumeric49 unique values
4 missing
predation_indexnumeric5 unique values
0 missing
sleep_exposure_indexnumeric5 unique values
0 missing
total_sleepnumeric44 unique values
4 missing

107 properties

62
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
12
Number of missing values in the dataset.
11
Number of instances with at least one value missing.
8
Number of numeric attributes.
0
Number of nominal attributes.
-0.48
Average class difference between consecutive instances.
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
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
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
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
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
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
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
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
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
Entropy of the target attribute values.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.13
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
45.74
Maximum kurtosis among attributes of the numeric type.
283.13
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
6.56
Maximum skewness among attributes of the numeric type.
930.28
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
9.56
Mean kurtosis among attributes of the numeric type.
82.82
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Average number of distinct values among the attributes of the nominal type.
2.1
Mean skewness among attributes of the numeric type.
250.45
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.33
Minimum kurtosis among attributes of the numeric type.
2.42
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0.2
Minimum skewness among attributes of the numeric type.
1.44
Minimum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
17.74
Percentage of instances having missing values.
2.42
Percentage of missing values.
100
Percentage of numeric attributes.
0
Percentage of nominal attributes.
First quartile of entropy among attributes.
-1.22
First quartile of kurtosis among attributes of the numeric type.
2.68
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
0.27
First quartile of skewness among attributes of the numeric type.
1.51
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
1.17
Second quartile (Median) of kurtosis among attributes of the numeric type.
15.21
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1.18
Second quartile (Median) of skewness among attributes of the numeric type.
11.41
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
21.17
Third quartile of kurtosis among attributes of the numeric type.
184.68
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
4.31
Third quartile of skewness among attributes of the numeric type.
711.07
Third quartile of standard deviation of 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.trees.REPTree -L 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Standard deviation of the number of distinct values among attributes of the nominal type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

7 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: danger_index
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: danger_index
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: danger_index
0 runs - estimation_procedure: Leave one out - target_feature: danger_index
0 runs - estimation_procedure: 10% Holdout set - target_feature: danger_index
0 runs - estimation_procedure: 33% Holdout set - target_feature: danger_index
0 runs - estimation_procedure: Test on Training Data - target_feature: danger_index
Define a new task