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  • binarized_regression_problem mythbusting_1 study_1 study_144 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').

35 features

binaryClass (target)nominal2 unique values
0 missing
FICEnumeric1302 unique values
0 missing
College_name (ignore)nominal1274 unique values
0 missing
Statenominal51 unique values
0 missing
Public/private_indicatornumeric2 unique values
0 missing
Average_Math_SAT_scorenumeric248 unique values
525 missing
Average_Verbal_SAT_scorenumeric222 unique values
525 missing
Average_Combined_SAT_scorenumeric339 unique values
523 missing
Average_ACT_scorenumeric17 unique values
588 missing
First_quartile-Math_SATnumeric85 unique values
530 missing
Third_quartile-Math_SATnumeric85 unique values
530 missing
First_quartile-Verbal_SATnumeric66 unique values
530 missing
Third_quartile-Verbal_SATnumeric85 unique values
530 missing
First_quartile-ACTnumeric20 unique values
639 missing
Third_quartile-ACTnumeric19 unique values
639 missing
Number_of_applications_receivednumeric1127 unique values
10 missing
Number_of_applicants_acceptednumeric1065 unique values
11 missing
Number_of_new_students_enrollednumeric870 unique values
5 missing
Pct._new_students_from_top_10Perc_of_H.S._classnumeric90 unique values
235 missing
Pct._new_students_from_top_25Perc_of_H.S._classnumeric93 unique values
202 missing
Number_of_fulltime_undergraduatesnumeric1151 unique values
3 missing
Number_of_parttime_undergraduatesnumeric883 unique values
32 missing
In-state_tuitionnumeric948 unique values
30 missing
Out-of-state_tuitionnumeric963 unique values
20 missing
Room_and_board_costsnumeric798 unique values
76 missing
Room_costsnumeric598 unique values
321 missing
Board_costsnumeric465 unique values
498 missing
Additional_feesnumeric433 unique values
274 missing
Estimated_book_costsnumeric164 unique values
48 missing
Estimated_personal_spendingnumeric406 unique values
181 missing
Pct._of_faculty_with_Ph.D.snumeric90 unique values
32 missing
Pct._of_faculty_with_terminal_degreenumeric77 unique values
30 missing
Student/faculty_rationumeric208 unique values
2 missing
Pct.alumni_who_donatenumeric62 unique values
222 missing
Instructional_expenditure_per_studentnumeric1181 unique values
39 missing

107 properties

1302
Number of instances (rows) of the dataset.
35
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
7830
Number of missing values in the dataset.
1144
Number of instances with at least one value missing.
32
Number of numeric attributes.
3
Number of nominal attributes.
0.74
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
688
Number of instances belonging to the most frequent class.
5.25
Minimal entropy among attributes.
540.21
Second quartile (Median) of means among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
5.25
Maximum entropy among attributes.
-1.67
Minimum kurtosis among attributes of the numeric type.
0.11
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
50.08
Maximum kurtosis among attributes of the numeric type.
1.64
Minimum of means among attributes of the numeric type.
0.63
Second quartile (Median) of skewness among attributes of the numeric type.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
9276.91
Maximum of means among attributes of the numeric type.
0.11
Minimal mutual information between the nominal attributes and the target attribute.
2.86
Percentage of binary attributes.
99.95
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Number of attributes divided by the number of instances.
0.11
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
87.86
Percentage of instances having missing values.
5.25
Third quartile of entropy among attributes.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
9.12
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
51
The maximum number of distinct values among attributes of the nominal type.
-0.62
Minimum skewness among attributes of the numeric type.
17.18
Percentage of missing values.
16.6
Third quartile of kurtosis among attributes of the numeric type.
0.59
Average class difference between consecutive instances.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.14
Maximum skewness among attributes of the numeric type.
0.48
Minimum standard deviation of attributes of the numeric type.
91.43
Percentage of numeric attributes.
2401.26
Third quartile of means among attributes of the numeric type.
0.73
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5348.16
Maximum standard deviation of attributes of the numeric type.
47.16
Percentage of instances belonging to the least frequent class.
8.57
Percentage of nominal attributes.
0.11
Third quartile of mutual information between the nominal attributes and the target attribute.
0.29
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
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5.25
Average entropy of the attributes.
614
Number of instances belonging to the least frequent class.
5.25
First quartile of entropy among attributes.
2.81
Third quartile of skewness among attributes of the numeric type.
0.42
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
8.59
Mean kurtosis among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.17
First quartile of kurtosis among attributes of the numeric type.
1548.97
Third quartile of standard deviation of attributes of the numeric type.
0.73
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1716.37
Mean of means among attributes of the numeric type.
0.28
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
70.29
First quartile of means among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.29
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
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.11
Average mutual information between the nominal attributes and the target attribute.
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.11
First quartile of mutual information between the nominal attributes and the target attribute.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.42
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
46.98
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.
0.39
First quartile of skewness among attributes of the numeric type.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.73
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
34.65
Standard deviation of the number of distinct values among attributes of the nominal type.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
26.5
Average number of distinct values among the attributes of the nominal type.
18.95
First quartile of standard deviation of attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.29
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
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.5
Mean skewness among attributes of the numeric type.
5.25
Second quartile (Median) of entropy among attributes.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.42
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
0.38
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
52.84
Percentage of instances belonging to the most frequent class.
1117.11
Mean standard deviation of attributes of the numeric type.

7 tasks

104 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 - evaluation_measure: predictive_accuracy - 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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