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letter

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Author: David J. Slate Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Letter+Recognition) - 01-01-1991 Please cite: P. W. Frey and D. J. Slate. "Letter Recognition Using Holland-style Adaptive Classifiers". Machine Learning 6(2), 1991 1. TITLE: Letter Image Recognition Data The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000. See the article cited above for more details.

17 features

class (target)nominal26 unique values
0 missing
y2barnumeric16 unique values
0 missing
yegvxnumeric16 unique values
0 missing
y-egenumeric16 unique values
0 missing
xegvynumeric16 unique values
0 missing
x-egenumeric16 unique values
0 missing
xy2brnumeric16 unique values
0 missing
x2ybrnumeric16 unique values
0 missing
xybarnumeric16 unique values
0 missing
x-boxnumeric16 unique values
0 missing
x2barnumeric16 unique values
0 missing
y-barnumeric16 unique values
0 missing
x-barnumeric16 unique values
0 missing
onpixnumeric16 unique values
0 missing
highnumeric16 unique values
0 missing
widthnumeric16 unique values
0 missing
y-boxnumeric16 unique values
0 missing

107 properties

20000
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
26
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.
16
Number of numeric attributes.
1
Number of nominal attributes.
0.04
Average class difference between consecutive instances.
0.94
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
0.15
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
0.84
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
0.94
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
0.15
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
0.84
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
0.94
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
0.15
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
0.84
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
4.7
Entropy of the target attribute values.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.93
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.07
Percentage of instances belonging to the most frequent class.
813
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
2.08
Maximum kurtosis among attributes of the numeric type.
8.34
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
26
The maximum number of distinct values among attributes of the nominal type.
1.16
Maximum skewness among attributes of the numeric type.
3.3
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
0.7
Mean kurtosis among attributes of the numeric type.
5.93
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
26
Average number of distinct values among the attributes of the nominal type.
0.29
Mean skewness among attributes of the numeric type.
2.27
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.42
Minimum kurtosis among attributes of the numeric type.
3.05
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
26
The minimal number of distinct values among attributes of the nominal type.
-0.31
Minimum skewness among attributes of the numeric type.
1.55
Minimum standard deviation of attributes of the numeric type.
3.67
Percentage of instances belonging to the least frequent class.
734
Number of instances belonging to the least frequent class.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.36
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
94.12
Percentage of numeric attributes.
5.88
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.31
First quartile of kurtosis among attributes of the numeric type.
4.17
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.14
First quartile of skewness among attributes of the numeric type.
2.02
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.89
Second quartile (Median) of kurtosis among attributes of the numeric type.
5.91
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.25
Second quartile (Median) of skewness among attributes of the numeric type.
2.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
1.17
Third quartile of kurtosis among attributes of the numeric type.
7.73
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.65
Third quartile of skewness among attributes of the numeric type.
2.55
Third quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Define a new task