Data
Weather

Weather

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The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some unspecified game. In general, instances in a dataset are characterized by the values of features, or attributes, that measure different aspects of the instance. In this case there are four attributes: outlook, temperature, humidity, and windy. The outcome is whether to play or not.

5 features

play (target)nominal2 unique values
0 missing
outlooknominal3 unique values
0 missing
temperaturenumeric12 unique values
0 missing
humiditynumeric10 unique values
0 missing
windynominal1 unique values
0 missing

107 properties

14
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
2
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.
2
Number of numeric attributes.
3
Number of nominal attributes.
0.54
Average class difference between consecutive instances.
0.34
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
0.43
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
-0.14
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
0.34
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
0.43
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
-0.14
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
0.34
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
0.43
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
-0.14
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
0.94
Entropy of the target attribute values.
0.3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.57
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.36
Number of attributes divided by the number of instances.
7.62
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.57
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.57
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.57
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
64.29
Percentage of instances belonging to the most frequent class.
9
Number of instances belonging to the most frequent class.
1.58
Maximum entropy among attributes.
-0.88
Maximum kurtosis among attributes of the numeric type.
81.64
Maximum of means among attributes of the numeric type.
0.25
Maximum mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
0.37
Maximum skewness among attributes of the numeric type.
10.29
Maximum standard deviation of attributes of the numeric type.
0.79
Average entropy of the attributes.
-1.12
Mean kurtosis among attributes of the numeric type.
77.61
Mean of means among attributes of the numeric type.
0.12
Average mutual information between the nominal attributes and the target attribute.
5.39
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Average number of distinct values among the attributes of the nominal type.
0.09
Mean skewness among attributes of the numeric type.
8.43
Mean standard deviation of attributes of the numeric type.
-0
Minimal entropy among attributes.
-1.37
Minimum kurtosis among attributes of the numeric type.
73.57
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
1
The minimal number of distinct values among attributes of the nominal type.
-0.18
Minimum skewness among attributes of the numeric type.
6.57
Minimum standard deviation of attributes of the numeric type.
35.71
Percentage of instances belonging to the least frequent class.
5
Number of instances belonging to the least frequent class.
0.33
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.43
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2
Number of binary attributes.
40
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
40
Percentage of numeric attributes.
60
Percentage of nominal attributes.
-0
First quartile of entropy among attributes.
-1.37
First quartile of kurtosis among attributes of the numeric type.
73.57
First quartile of means among attributes of the numeric type.
0
First quartile of mutual information between the nominal attributes and the target attribute.
-0.18
First quartile of skewness among attributes of the numeric type.
6.57
First quartile of standard deviation of attributes of the numeric type.
0.79
Second quartile (Median) of entropy among attributes.
-1.12
Second quartile (Median) of kurtosis among attributes of the numeric type.
77.61
Second quartile (Median) of means among attributes of the numeric type.
0.12
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.09
Second quartile (Median) of skewness among attributes of the numeric type.
8.43
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.58
Third quartile of entropy among attributes.
-0.88
Third quartile of kurtosis among attributes of the numeric type.
81.64
Third quartile of means among attributes of the numeric type.
0.25
Third quartile of mutual information between the nominal attributes and the target attribute.
0.37
Third quartile of skewness among attributes of the numeric type.
10.29
Third quartile of standard deviation of attributes of the numeric type.
0.42
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.36
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.42
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.36
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.42
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.36
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.33
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.57
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
-0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.33
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.57
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
-0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.57
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
-0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Standard deviation of the number of distinct values among attributes of the nominal type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.29
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

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