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tamilnadu-electricity

tamilnadu-electricity

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Author: K.Kalyani. Source: UCI Please cite: * Title: Tamilnadu Electricity Board Hourly Readings Data Set * Abstract: This data can be effectively produced the result to fewer parameter of the Load profile can be reduced in the Database * Source: K.Kalyani ,kkalyanims '@' gmail.com,T.U.K Arts College,Karanthai,Thanjavur. * Data Set Information: Collect the real time readings for residential,commercial,industrial,agriculure,to find the accuracy consumption in Tamil Nadu Around Thanajvur. * Attribute Information: forkva,forkw,type,sector,service * Relevant Papers: Efficient Electricity Utilization By IHBMO

4 features

Class (target)nominal20 unique values
0 missing
V1numeric44778 unique values
0 missing
V2numeric44777 unique values
0 missing
V3nominal31 unique values
0 missing

107 properties

45781
Number of instances (rows) of the dataset.
4
Number of attributes (columns) of the dataset.
20
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.
2
Number of nominal attributes.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.5
Maximum of means among attributes of the numeric type.
4.25
Minimal mutual information between the nominal attributes and the target attribute.
0.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
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.
4.25
Maximum mutual information between the nominal attributes and the target attribute.
20
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
4.94
Third quartile of entropy among attributes.
0
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.
31
The maximum number of distinct values among attributes of the nominal type.
-0.01
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
-1.2
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
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
-0
Maximum skewness among attributes of the numeric type.
0.29
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.5
Third quartile of means 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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.29
Maximum standard deviation of attributes of the numeric type.
3.05
Percentage of instances belonging to the least frequent class.
50
Percentage of numeric attributes.
4.25
Third quartile of mutual information between the nominal attributes and the target attribute.
0
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
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
4.94
Average entropy of the attributes.
1397
Number of instances belonging to the least frequent class.
50
Percentage of nominal attributes.
-0
Third quartile of skewness among attributes of the numeric type.
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
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
-1.2
Mean kurtosis among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.94
First quartile of entropy among attributes.
0.29
Third quartile of standard deviation of attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.5
Mean of means among attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.2
First quartile of kurtosis among attributes of the numeric type.
0.5
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
0
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
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
4.25
Average mutual information between the nominal attributes and the target attribute.
1
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.25
First quartile of mutual information between the nominal attributes and the target attribute.
0
Error rate 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
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
0.16
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.
-0.01
First quartile of skewness among attributes of the numeric type.
1
Kappa coefficient 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
7.78
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
25.5
Average number of distinct values among the attributes of the nominal type.
0.29
First quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
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
Mean skewness among attributes of the numeric type.
4.94
Second quartile (Median) of entropy among attributes.
0
Error rate 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
0
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
6.35
Percentage of instances belonging to the most frequent class.
0.29
Mean standard deviation of attributes of the numeric type.
-1.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
4.25
Entropy of the target attribute values.
1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
2906
Number of instances belonging to the most frequent class.
4.94
Minimal entropy among attributes.
0.5
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4.94
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
4.25
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-1.2
Maximum kurtosis among attributes of the numeric type.
0.5
Minimum of means among attributes of the numeric type.

11 tasks

8 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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