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tae

tae

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Author: Source: Unknown - Please cite: 1. Title: Teaching Assistant Evaluation 2. Sources: (a) Collector: Wei-Yin Loh (Department of Statistics, UW-Madison) (b) Donor: Tjen-Sien Lim (limt@stat.wisc.edu) (b) Date: June 7, 1997 3. Past Usage: 1. Loh, W.-Y. & Shih, Y.-S. (1997). Split Selection Methods for Classification Trees, Statistica Sinica 7: 815-840. 2. Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Machine Learning. Forthcoming. (ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or (http://www.stat.wisc.edu/~limt/mach1317.pdf) 4. Relevant Information: The data consist of evaluations of teaching performance over three regular semesters and two summer semesters of 151 teaching assistant (TA) assignments at the Statistics Department of the University of Wisconsin-Madison. The scores were divided into 3 roughly equal-sized categories ("low", "medium", and "high") to form the class variable. 5. Number of Instances: 151 6. Number of Attributes: 6 (including the class attribute) 7. Attribute Information: 1. Whether of not the TA is a native English speaker (binary) 1=English speaker, 2=non-English speaker 2. Course instructor (categorical, 25 categories) 3. Course (categorical, 26 categories) 4. Summer or regular semester (binary) 1=Summer, 2=Regular 5. Class size (numerical) 6. Class attribute (categorical) 1=Low, 2=Medium, 3=High 8. Missing Attribute Values: None Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

6 features

Class_attribute (target)nominal3 unique values
0 missing
Whether_of_not_the_TA_is_a_native_English_speakernominal2 unique values
0 missing
Course_instructornumeric25 unique values
0 missing
Coursenumeric26 unique values
0 missing
Summer_or_regular_semesternominal2 unique values
0 missing
Class_sizenumeric46 unique values
0 missing

107 properties

151
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
3
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.
3
Number of numeric attributes.
3
Number of nominal attributes.
-1.15
First quartile of kurtosis among attributes of the numeric type.
12.89
Third quartile of standard deviation of attributes of the numeric type.
0.61
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
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.46
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
16.54
Mean of means among attributes of the numeric type.
0.44
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
8.11
First quartile of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.53
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.06
Average mutual information between the nominal attributes and the target attribute.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.06
First quartile of mutual information between the nominal attributes and the target attribute.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.21
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
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
10.22
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
-0.01
First quartile of skewness among attributes of the numeric type.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.61
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
0.58
Standard deviation of the number of distinct values among attributes of the nominal type.
0.46
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.33
Average number of distinct values among the attributes of the nominal type.
6.83
First quartile of standard deviation of attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.53
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.45
Mean skewness among attributes of the numeric type.
0.66
Second quartile (Median) of entropy among attributes.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.21
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
0.42
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
34.44
Percentage of instances belonging to the most frequent class.
8.91
Mean standard deviation of attributes of the numeric type.
-0.5
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.58
Entropy of the target attribute values.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
52
Number of instances belonging to the most frequent class.
0.62
Minimal entropy among attributes.
13.64
Second quartile (Median) of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.71
Maximum entropy among attributes.
-1.15
Minimum kurtosis among attributes of the numeric type.
0.06
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.5
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.61
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.35
Maximum kurtosis among attributes of the numeric type.
8.11
Minimum of means among attributes of the numeric type.
0.5
Second quartile (Median) of skewness among attributes of the numeric type.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
27.87
Maximum of means among attributes of the numeric type.
0.06
Minimal mutual information between the nominal attributes and the target attribute.
7.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
0.06
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
33.33
Percentage of binary attributes.
0.71
Third quartile of entropy among attributes.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
26.92
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3
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.
-0.35
Third quartile of kurtosis among attributes of the numeric type.
0.93
Average class difference between consecutive instances.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.87
Maximum skewness among attributes of the numeric type.
6.83
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
27.87
Third quartile of means among attributes of the numeric type.
0.61
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
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.46
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
12.89
Maximum standard deviation of attributes of the numeric type.
32.45
Percentage of instances belonging to the least frequent class.
50
Percentage of numeric attributes.
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
0.53
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.66
Average entropy of the attributes.
49
Number of instances belonging to the least frequent class.
50
Percentage of nominal attributes.
0.62
First quartile of entropy among attributes.
0.87
Third quartile of skewness among attributes of the numeric type.
0.21
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
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.67
Mean kurtosis among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

11 tasks

940 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class_attribute
324 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class_attribute
311 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class_attribute
172 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class_attribute
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Class_attribute
182 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class_attribute
74 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class_attribute
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class_attribute
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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