Data
ada_agnostic

ada_agnostic

deactivated ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
0 likes downloaded by 14 people , 17 total downloads 0 issues 0 downvotes
  • OpenML100 study_1 study_123 study_14 study_34 study_41 study_52 study_7
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: [Isabelle Guyon](isabelle@clopinet.com) Source: [Agnostic Learning vs. Prior Knowledge Challenge](http://www.agnostic.inf.ethz.ch) Please cite: None Dataset from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLVA, GINA, NOVA, HIVA, ADA). The purpose of the challenge was to check if the performance of domain-specific feature engineering (prior knowledge) can be met by algorithms that were trained on data without any domain-specific knowledge (agnostic). For the latter, the data was anonymised and preprocessed in a way that makes them uninterpretable. This dataset contains the agnostic (smashed) version of a data set from the US census bureau for the time span June 2005 - September 2006. Similar data set on OpenML is called __adult__. The raw data from the census bureau is also known as the Adult database in the UCI machine-learning repository. ### Topic The task of ADA is to discover high revenue people from census data. This is a two-class classification problem. The raw data from the census bureau is known as the Adult database in the UCI machine-learning repository. It contains continuous, binary and categorical variables. The “prior knowledge track” has access to the original features and their identity. The agnostic track has access to a preprocessed numeric representation eliminating categorical variables. ### Source Original owners This data was extracted from the census bureau database found at http://www.census.gov/ftp/pub/DES/www/welcome.html Donor: Ronny Kohavi and Barry Becker, Data Mining and Visualization Silicon Graphics. e-mail: ronnyk@sgi.com for questions Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php ### Preprocessing In [this documentation](http://clopinet.com/isabelle/Projects/agnostic/Dataset.pdf) the organisers of the challenge describe the steps they performed to come up with the __agnostic__ data. The 14 original attributes (features) include age, workclass, education, marital status, occupation, native country, etc. It contains continuous, binary and categorical features. This dataset is from the "agnostic learning track", i.e. has access to a preprocessed numeric representation eliminating categorical variables, but the identity of the features is not revealed. ### Additional Info This dataset contains samples from both training and validation datasets. Modified by TunedIT (converted to ARFF format). Data type: non-sparse Number of features: 48 Number of examples and check-sums: Pos_ex Neg_ex Tot_ex Check_sum Train 1029 3118 4147 6798109.00 Valid 103 312 415 681151.00

49 features

label (target)nominal2 unique values
0 missing
attr0numeric2 unique values
0 missing
attr1numeric77 unique values
0 missing
attr2numeric2 unique values
0 missing
attr3numeric2 unique values
0 missing
attr4numeric2 unique values
0 missing
attr5numeric2 unique values
0 missing
attr6numeric2 unique values
0 missing
attr7numeric2 unique values
0 missing
attr8numeric2 unique values
0 missing
attr9numeric2 unique values
0 missing
attr10numeric2 unique values
0 missing
attr11numeric2 unique values
0 missing
attr12numeric2 unique values
0 missing
attr13numeric2 unique values
0 missing
attr14numeric363 unique values
0 missing
attr15numeric2 unique values
0 missing
attr16numeric2 unique values
0 missing
attr17numeric16 unique values
0 missing
attr18numeric2 unique values
0 missing
attr19numeric70 unique values
0 missing
attr20numeric2 unique values
0 missing
attr21numeric2 unique values
0 missing
attr22numeric2 unique values
0 missing
attr23numeric52 unique values
0 missing
attr24numeric2 unique values
0 missing
attr25numeric2 unique values
0 missing
attr26numeric2 unique values
0 missing
attr27numeric2 unique values
0 missing
attr28numeric2 unique values
0 missing
attr29numeric56 unique values
0 missing
attr30numeric2 unique values
0 missing
attr31numeric2 unique values
0 missing
attr32numeric2 unique values
0 missing
attr33numeric2 unique values
0 missing
attr34numeric2 unique values
0 missing
attr35numeric2 unique values
0 missing
attr36numeric2 unique values
0 missing
attr37numeric2 unique values
0 missing
attr38numeric2 unique values
0 missing
attr39numeric1 unique values
0 missing
attr40numeric2 unique values
0 missing
attr41numeric2 unique values
0 missing
attr42numeric2 unique values
0 missing
attr43numeric2 unique values
0 missing
attr44numeric2 unique values
0 missing
attr45numeric2 unique values
0 missing
attr46numeric2 unique values
0 missing
attr47numeric2 unique values
0 missing

107 properties

4562
Number of instances (rows) of the dataset.
49
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.
48
Number of numeric attributes.
1
Number of nominal attributes.
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.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.55
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
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
2.04
First quartile of skewness among attributes of the numeric type.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.85
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.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
7.31
Mean skewness among attributes of the numeric type.
0.18
First quartile of standard deviation of attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
14.35
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.55
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
0.23
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
75.19
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
9.61
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.81
Entropy of the target attribute values.
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
3430
Number of instances belonging to the most frequent class.
-2
Minimum kurtosis among attributes of the numeric type.
0.09
Second quartile (Median) of means among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4562
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
3.41
Second quartile (Median) of skewness among attributes of the numeric type.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
634.02
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
2.04
Percentage of binary attributes.
0.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-1.93
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.21
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.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
26.31
Third quartile of kurtosis among attributes of the numeric type.
0.63
Average class difference between consecutive instances.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
67.54
Maximum skewness among attributes of the numeric type.
24.81
Percentage of instances belonging to the least frequent class.
97.96
Percentage of numeric attributes.
0.3
Third quartile of means among attributes of the numeric type.
0.85
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
158.02
Maximum standard deviation of attributes of the numeric type.
1132
Number of instances belonging to the least frequent class.
2.04
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.16
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
5.32
Third quartile of skewness among attributes of the numeric type.
0.55
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
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
263.56
Mean kurtosis among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.67
First quartile of kurtosis among attributes of the numeric type.
0.41
Third quartile of standard deviation of attributes of the numeric type.
0.85
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
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
34.16
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.03
First quartile of means among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

18 tasks

102528 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: label
196 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: label
0 runs - estimation_procedure: 33% Holdout set - target_feature: label
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: label
83 runs - estimation_procedure: 10-fold Learning Curve - target_feature: label
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: label
0 runs - estimation_procedure: 50 times Clustering
0 runs - target_feature: label
0 runs - estimation_procedure: 50 times Clustering
1306 runs - target_feature: label
1304 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
Define a new task