Data
water-treatment

water-treatment

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized binarized_regression_problem mythbusting_1 study_1 study_15 study_20
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

39 features

binaryClass (target)nominal2 unique values
0 missing
date (ignore)nominal527 unique values
0 missing
Q-E (ignore)nominal503 unique values
18 missing
ZN-Enumeric168 unique values
3 missing
PH-Enumeric16 unique values
0 missing
DBO-Enominal204 unique values
23 missing
DQO-Enominal288 unique values
6 missing
SS-Enominal141 unique values
1 missing
SSV-Enumeric274 unique values
11 missing
SED-Enumeric59 unique values
25 missing
COND-Enominal414 unique values
0 missing
PH-Pnumeric13 unique values
0 missing
DBO-Pnominal225 unique values
40 missing
SS-Pnominal154 unique values
0 missing
SSV-Pnumeric284 unique values
11 missing
SED-Pnumeric62 unique values
24 missing
COND-Pnominal412 unique values
0 missing
PH-Dnumeric13 unique values
0 missing
DBO-Dnominal148 unique values
28 missing
DQO-Dnominal229 unique values
9 missing
SS-Dnominal74 unique values
2 missing
SSV-Dnumeric242 unique values
13 missing
SED-Dnumeric22 unique values
25 missing
COND-Dnominal410 unique values
0 missing
PH-Snumeric15 unique values
1 missing
DBO-Snominal43 unique values
23 missing
DQO-Snominal136 unique values
18 missing
SS-Snominal57 unique values
5 missing
SSV-Snumeric192 unique values
17 missing
SED-Snumeric17 unique values
28 missing
COND-Snominal412 unique values
1 missing
RD-DBO-Pnumeric314 unique values
62 missing
RD-SS-Pnumeric307 unique values
4 missing
RD-SED-Pnumeric143 unique values
27 missing
RD-DBO-Snumeric184 unique values
40 missing
RD-DQO-Snumeric264 unique values
26 missing
RD-DBO-Gnumeric155 unique values
36 missing
RD-DQO-Gnumeric229 unique values
25 missing
RD-SS-Gnumeric182 unique values
8 missing

107 properties

527
Number of instances (rows) of the dataset.
39
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
560
Number of missing values in the dataset.
146
Number of instances with at least one value missing.
21
Number of numeric attributes.
18
Number of nominal attributes.
0.8
Average class difference between consecutive instances.
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
13.9
Maximum skewness among attributes of the numeric type.
0.19
Minimum standard deviation of attributes of the numeric type.
2.72
Percentage of missing values.
35.63
Third quartile of kurtosis among attributes of the numeric type.
0.97
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
0.52
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
14.79
Maximum standard deviation of attributes of the numeric type.
15.18
Percentage of instances belonging to the least frequent class.
53.85
Percentage of numeric attributes.
79
Third quartile of means among attributes of the numeric type.
0.03
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
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
7.1
Average entropy of the attributes.
80
Number of instances belonging to the least frequent class.
46.15
Percentage of nominal attributes.
0.48
Third quartile of mutual information between the nominal attributes and the target attribute.
0.88
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
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
26.27
Mean kurtosis among attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
6.63
First quartile of entropy among attributes.
2.72
Third quartile of skewness among attributes of the numeric type.
11.04
Third quartile of standard deviation of attributes of the numeric type.
0.97
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
0.52
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
43.51
Mean of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.13
First quartile of kurtosis among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
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
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.3
Average mutual information between the nominal attributes and the target attribute.
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
6.37
First quartile of means among attributes of the numeric type.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.88
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
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
22.44
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
0.22
First quartile of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
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
141.47
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
209.31
Average number of distinct values among the attributes of the nominal type.
-1.64
First quartile of skewness among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.46
Mean skewness among attributes of the numeric type.
0.31
First quartile of standard deviation of attributes of the numeric type.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.88
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
0.22
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
84.82
Percentage of instances belonging to the most frequent class.
6.47
Mean standard deviation of attributes of the numeric type.
7.27
Second quartile (Median) of entropy among attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.61
Entropy of the target attribute values.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
447
Number of instances belonging to the most frequent class.
4.76
Minimal entropy among attributes.
6.83
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
8.58
Maximum entropy among attributes.
-0.31
Minimum kurtosis among attributes of the numeric type.
58.52
Second quartile (Median) of means among attributes of the numeric type.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
218.31
Maximum kurtosis among attributes of the numeric type.
0.04
Minimum of means among attributes of the numeric type.
0.25
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
90.55
Maximum of means among attributes of the numeric type.
0.09
Minimal mutual information between the nominal attributes and the target attribute.
-0.36
Second quartile (Median) of skewness among attributes of the numeric type.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.07
Number of attributes divided by the number of instances.
0.5
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
2.56
Percentage of binary attributes.
8.22
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
2.03
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
414
The maximum number of distinct values among attributes of the nominal type.
-5.61
Minimum skewness among attributes of the numeric type.
27.7
Percentage of instances having missing values.
8.56
Third quartile of entropy among attributes.

7 tasks

102 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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