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creditcard

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  • Classification Creditcard Finance Fraud Kaggle Unbalanced WebAnalytics2015
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Author: Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi Source: Credit card fraud detection - Date 25th of June 2015 Please cite: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.

31 features

Class (target)nominal2 unique values
0 missing
Time (row identifier)numeric124592 unique values
0 missing
V1numeric275663 unique values
0 missing
V2numeric275663 unique values
0 missing
V3numeric275663 unique values
0 missing
V4numeric275663 unique values
0 missing
V5numeric275663 unique values
0 missing
V6numeric275663 unique values
0 missing
V7numeric275663 unique values
0 missing
V8numeric275663 unique values
0 missing
V9numeric275663 unique values
0 missing
V10numeric275663 unique values
0 missing
V11numeric275663 unique values
0 missing
V12numeric275663 unique values
0 missing
V13numeric275663 unique values
0 missing
V14numeric275663 unique values
0 missing
V15numeric275663 unique values
0 missing
V16numeric275663 unique values
0 missing
V17numeric275663 unique values
0 missing
V18numeric275663 unique values
0 missing
V19numeric275663 unique values
0 missing
V20numeric275663 unique values
0 missing
V21numeric275663 unique values
0 missing
V22numeric275663 unique values
0 missing
V23numeric275663 unique values
0 missing
V24numeric275663 unique values
0 missing
V25numeric275663 unique values
0 missing
V26numeric275663 unique values
0 missing
V27numeric275663 unique values
0 missing
V28numeric275663 unique values
0 missing
Amountnumeric32767 unique values
0 missing

107 properties

284807
Number of instances (rows) of the dataset.
31
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.
30
Number of numeric attributes.
1
Number of nominal attributes.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.87
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
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
2
Average number of distinct values among the attributes of the nominal type.
-2.26
First quartile of skewness among attributes of the numeric type.
0.9
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.05
Mean skewness among attributes of the numeric type.
0.73
First quartile of standard deviation of attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.75
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
99.83
Percentage of instances belonging to the most frequent class.
9.57
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.02
Entropy of the target attribute values.
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
284315
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
26.62
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.2
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
933.4
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
-0.31
Second quartile (Median) of skewness among attributes of the numeric type.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
88.35
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.96
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.86
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.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
3.23
Percentage of binary attributes.
Third quartile of entropy among attributes.
0
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.
2
The maximum number of distinct values among attributes of the nominal type.
-8.52
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
213.94
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
16.98
Maximum skewness among attributes of the numeric type.
0.33
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0
Third quartile of means among attributes of the numeric type.
0.87
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
0.86
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
250.12
Maximum standard deviation of attributes of the numeric type.
0.17
Percentage of instances belonging to the least frequent class.
96.77
Percentage of numeric attributes.
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
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
492
Number of instances belonging to the least frequent class.
3.23
Percentage of nominal attributes.
0.63
Third quartile of skewness among attributes of the numeric type.
0.75
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
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
143.97
Mean kurtosis among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.28
Third quartile of standard deviation of attributes of the numeric type.
0.87
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
0.86
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
3.05
Mean of means among attributes of the numeric type.
0.02
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.61
First quartile of kurtosis among attributes of the numeric type.
0.9
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
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
First quartile of means among attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.75
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
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.85
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.

18 tasks

350 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: total_cost - target_feature: Class - cost matrix: [[0,1],[500,0]]
4 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: Class
3 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: Class - cost matrix: [[0,1],[500,0]]
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: usercpu_time_millis - target_feature: Class - cost matrix: [[0,1],[500,0]]
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: f_measure - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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