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dna

dna

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  • OpenML-CC18 study_135 study_98 study_99 study_293 study_270 study_271 study_253 study_275
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Author: Ross King, based on data from Genbank 64.1 Source: [MLbench](https://www.rdocumentation.org/packages/mlbench/versions/2.1-1/topics/DNA). Originally from the StatLog project. Please Cite: Primate Splice-Junction Gene Sequences (DNA) Originally from the StatLog project. The raw data is still available on [UCI](https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Splice-junction+Gene+Sequences)). The data consists of 3,186 data points (splice junctions). The data points are described by 180 indicator binary variables and the problem is to recognize the 3 classes (ei, ie, neither), i.e., the boundaries between exons (the parts of the DNA sequence retained after splicing) and introns (the parts of the DNA sequence that are spliced out). The StatLog DNA dataset is a processed version of the [Irvine database]((https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+(Splice-junction+Gene+Sequences))). The main difference is that the symbolic variables representing the nucleotides (only A,G,T,C) were replaced by 3 binary indicator variables. Thus the original 60 symbolic attributes were changed into 180 binary attributes. The names of the examples were removed. The examples with ambiguities were removed (there was very few of them, 4). The StatLog version of this dataset was produced by Ross King at Strathclyde University. For original details see the Irvine database documentation. The nucleotides A,C,G,T were given indicator values as follows: A -> 1 0 0 C -> 0 1 0 G -> 0 0 1 T -> 0 0 0 Hint: Much better performance is generally observed if attributes closest to the junction are used. In the StatLog version, this means using attributes A61 to A120 only.

181 features

class (target)nominal3 unique values
0 missing
A0nominal2 unique values
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A1nominal2 unique values
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A2nominal2 unique values
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A3nominal2 unique values
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Show first 100 features

62 properties

NumberOfInstances
3186
Number of instances (rows) of the dataset.
NumberOfFeatures
181
Number of attributes (columns) of the dataset.
NumberOfClasses
3
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
0
Number of numeric attributes.
NumberOfSymbolicFeatures
181
Number of nominal attributes.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
99.45
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.77
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0.07
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.81
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.85
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.38
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
ClassEntropy
1.48
Entropy of the target attribute values.
Dimensionality
0.06
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
68.43
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
51.91
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1654
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.38
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.81
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
180
Number of binary attributes.
MeanMutualInformation
0.02
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
36.27
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.01
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.58
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
24.01
Percentage of instances belonging to the least frequent class.
MinorityClassSize
765
Number of instances belonging to the least frequent class.
Show all 62 properties

27 tasks

Supervised Classification on dna
4967 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on dna
2096 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on dna
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on dna
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on dna
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on dna
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on dna
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on dna
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering - target_feature: tubercolosis
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
Clustering on dna
0 runs - estimation_procedure: 50 times Clustering
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