metafeatures: Extract meta-features from a dataset
In mfe: Meta-Feature Extractor
Description
Usage
Arguments
Details
Value
Examples
Description
This is a simple way to extract the meta-features from a dataset, where all
meta-features from each group is extracted.
Usage
1
2
3
4
5
6
7
metafeatures(...)
## Default S3 method:
metafeatures(x, y, groups = "default", summary = c("mean", "sd"), ...)
## S3 method for class 'formula'
metafeatures(formula, data, groups = "default", summary = c("mean", "sd"), ...)
Arguments
...
Optional arguments to the summary methods.
x
A data.frame contained only the input attributes.
y
A factor response vector with one label for each row/component of x.
groups
A list of meta-features groups, "default" for traditional
groups of meta-features or "all" to include all them. The details
section describes the valid values for this parameter.
summary
A list of summarization functions or empty for all values. See
post.processing method to more information. (Default:
c("mean", "sd"))
formula
A formula to define the class column.
data
A data.frame dataset contained the input attributes and class
The details section describes the valid values for this group.
Details
The following groups are allowed for this method:
- "infotheo"
Include all information theoretical meta-features. See
infotheo for more details.
- "general"
Include all general (simple) meta-features. See
general for more details.
- "landmarking"
Include all landmarking meta-features. See
landmarking for more details.
- "model.based"
Include all model based meta-features. See
model.based for more details.
- "statistical"
Include all statistical meta-features. See
statistical for more details.
- "clustering"
Include all clustering meta-features. See
clustering for more details.
- "complexity"
Include all complexity meta-features. See
complexity for more details.
- "concept"
Include all concept variation meta-features. See
concept for more details.
- "itemset"
Include all itemset meta-features. See
itemset for more details.
Value
A numeric vector named by the meta-features from the specified
groups.
Examples
1
2
3
4
5
6
7
8
## Extract all meta-features
metafeatures(Species ~ ., iris)
## Extract some groups of meta-features
metafeatures(iris[1:4], iris[5], c("general", "statistical", "infotheo"))
## Use another summary methods
metafeatures(Species ~ ., iris, summary=c("min", "median", "max"))
Example output
discriminant.cancor
0.98482089
discriminant.cancor.fract
0.81372024
discriminant.center.of.gravity
3.20828116
discriminant.discfct
0.66666667
discriminant.eigen.fract
0.92461872
discriminant.max.eigenvalue
4.22824171
discriminant.min.eighenvalue
0.02383509
discriminant.sdratio
1.27722888
discriminant.wlambda
0.34245841
general.defective.instances
0.00000000
general.dimensionality
0.02666667
general.majority.class
0.33333333
general.missing.values
0.00000000
general.nattribute
4.00000000
general.nbinary
0.00000000
general.nclasse
3.00000000
general.ninstance
150.00000000
general.nnumeric
4.00000000
general.nsymbolic
0.00000000
general.pbinary
0.00000000
general.pnumeric
1.00000000
general.psymbolic
0.00000000
general.sdclass
0.00000000
infotheo.attributes.concentration.mean
0.20980486
infotheo.attributes.concentration.sd
0.11958799
infotheo.attribute.entropy.mean
0.98073290
infotheo.attribute.entropy.sd
0.02628825
infotheo.class.concentration.mean
0.51481787
infotheo.class.concentration.sd
0.25900418
infotheo.class.entropy
1.00000000
infotheo.equivalent.attributes
1.87806411
infotheo.joint.entropy.mean
3.01821959
infotheo.joint.entropy.sd
0.38218827
infotheo.mutual.information.mean
0.84393418
infotheo.mutual.information.sd
0.42220265
infotheo.noise.signal
1.69830435
landmarking.decision.stumps.mean
0.94444444
landmarking.decision.stumps.sd
0.06085806
landmarking.elite.nearest.neighbor.mean
0.96666667
landmarking.elite.nearest.neighbor.sd
0.04548588
landmarking.linear.discriminant.mean
0.88888889
landmarking.linear.discriminant.sd
0.12904997
landmarking.naive.bayes.mean
0.94222222
landmarking.naive.bayes.sd
0.07161697
landmarking.nearest.neighbor.mean
0.97333333
landmarking.nearest.neighbor.sd
0.04143036
landmarking.worst.node.mean
0.82222222
landmarking.worst.node.sd
0.16913875
model.based.average.leaf.corrobation.mean
0.33333333
model.based.average.leaf.corrobation.sd
0.02666667
model.based.branch.length.mean
1.66666667
model.based.branch.length.sd
0.57735027
model.based.depth.mean
1.20000000
model.based.depth.sd
0.83666003
model.based.homogeneity.mean
6.00000000
model.based.homogeneity.sd
0.00000000
model.based.max.depth
2.00000000
model.based.nleave
3.00000000
model.based.nnode
2.00000000
model.based.nodes.per.attribute
0.50000000
model.based.nodes.per.instance
0.01333333
model.based.nodes.per.level.mean
1.00000000
model.based.nodes.per.level.sd
0.00000000
model.based.repeated.nodes.mean
0.50000000
model.based.repeated.nodes.sd
0.57735027
model.based.shape.mean
0.50000000
model.based.shape.sd
0.00000000
model.based.variable.importance.mean
22.24235105
model.based.variable.importance.sd
26.07505668
statistical.correlation.mean
0.48505297
statistical.correlation.sd
0.20939015
statistical.covariance.mean
0.07154263
statistical.covariance.sd
0.07130389
statistical.discreteness.degree.mean
1.00000000
statistical.discreteness.degree.sd
0.00000000
statistical.geometric.mean.mean
3.44476412
statistical.geometric.mean.sd
2.01825110
statistical.harmonic.mean.mean
3.46450000
statistical.harmonic.mean.sd
1.97548999
statistical.iqr.mean
1.28810588
statistical.iqr.sd
0.25053916
statistical.kurtosis.mean
0.55691608
statistical.kurtosis.sd
0.28615735
statistical.mad.mean
0.35211750
statistical.mad.sd
0.19259539
statistical.normality.mean
0.66666667
statistical.normality.sd
0.57735027
statistical.outliers.mean
0.00000000
statistical.outliers.sd
0.00000000
statistical.skewness.mean
0.29715985
statistical.skewness.sd
0.33328611
statistical.standard.deviation.mean
0.35776315
statistical.standard.deviation.sd
0.16137542
statistical.trim.mean.mean
3.45583333
statistical.trim.mean.sd
2.01128389
statistical.variance.mean
0.15186633
statistical.variance.sd
0.12214091
general.defective.instances general.dimensionality
0.00000000 0.02666667
general.majority.class general.missing.values
0.33333333 0.00000000
general.nattribute general.nbinary
4.00000000 0.00000000
general.nclasse general.ninstance
3.00000000 150.00000000
general.nnumeric general.nsymbolic
4.00000000 0.00000000
general.pbinary general.pnumeric
0.00000000 1.00000000
general.psymbolic general.sdclass
0.00000000 0.00000000
statistical.correlation.mean statistical.correlation.sd
0.48505297 0.20939015
statistical.covariance.mean statistical.covariance.sd
0.07154263 0.07130389
statistical.discreteness.degree.mean statistical.discreteness.degree.sd
1.00000000 0.00000000
statistical.geometric.mean.mean statistical.geometric.mean.sd
3.44476412 2.01825110
statistical.harmonic.mean.mean statistical.harmonic.mean.sd
3.46450000 1.97548999
statistical.iqr.mean statistical.iqr.sd
1.28810588 0.25053916
statistical.kurtosis.mean statistical.kurtosis.sd
0.55691608 0.28615735
statistical.mad.mean statistical.mad.sd
0.35211750 0.19259539
statistical.normality.mean statistical.normality.sd
0.66666667 0.57735027
statistical.outliers.mean statistical.outliers.sd
0.00000000 0.00000000
statistical.skewness.mean statistical.skewness.sd
0.29715985 0.33328611
statistical.standard.deviation.mean statistical.standard.deviation.sd
0.35776315 0.16137542
statistical.trim.mean.mean statistical.trim.mean.sd
3.45583333 2.01128389
statistical.variance.mean statistical.variance.sd
0.15186633 0.12214091
infotheo.attributes.concentration.mean infotheo.attributes.concentration.sd
0.20980486 0.11958799
infotheo.attribute.entropy.mean infotheo.attribute.entropy.sd
0.98073290 0.02628825
infotheo.class.concentration.mean infotheo.class.concentration.sd
0.51481787 0.25900418
infotheo.class.entropy infotheo.equivalent.attributes
1.00000000 1.87806411
infotheo.joint.entropy.mean infotheo.joint.entropy.sd
3.01821959 0.38218827
infotheo.mutual.information.mean infotheo.mutual.information.sd
0.84393418 0.42220265
infotheo.noise.signal
1.69830435
discriminant.cancor
9.848209e-01
discriminant.cancor.fract
8.137202e-01
discriminant.center.of.gravity
3.208281e+00
discriminant.discfct
6.666667e-01
discriminant.eigen.fract
9.246187e-01
discriminant.max.eigenvalue
4.228242e+00
discriminant.min.eighenvalue
2.383509e-02
discriminant.sdratio
1.277229e+00
discriminant.wlambda
3.424584e-01
general.defective.instances
0.000000e+00
general.dimensionality
2.666667e-02
general.majority.class
3.333333e-01
general.missing.values
0.000000e+00
general.nattribute
4.000000e+00
general.nbinary
0.000000e+00
general.nclasse
3.000000e+00
general.ninstance
1.500000e+02
general.nnumeric
4.000000e+00
general.nsymbolic
0.000000e+00
general.pbinary
0.000000e+00
general.pnumeric
1.000000e+00
general.psymbolic
0.000000e+00
general.sdclass
0.000000e+00
infotheo.attributes.concentration.min
8.478340e-02
infotheo.attributes.concentration.median
1.846740e-01
infotheo.attributes.concentration.max
4.299568e-01
infotheo.attribute.entropy.min
9.415587e-01
infotheo.attribute.entropy.median
9.920377e-01
infotheo.attribute.entropy.max
9.972975e-01
infotheo.class.concentration.min
2.227820e-01
infotheo.class.concentration.median
5.465452e-01
infotheo.class.concentration.max
7.433990e-01
infotheo.class.entropy
1.000000e+00
infotheo.equivalent.attributes
1.878064e+00
infotheo.joint.entropy.min
2.682002e+00
infotheo.joint.entropy.median
2.990150e+00
infotheo.joint.entropy.max
3.410577e+00
infotheo.mutual.information.min
3.606172e-01
infotheo.mutual.information.median
9.067693e-01
infotheo.mutual.information.max
1.201581e+00
infotheo.noise.signal
1.698304e+00
landmarking.decision.stumps.min
8.000000e-01
landmarking.decision.stumps.median
9.333333e-01
landmarking.decision.stumps.max
1.000000e+00
landmarking.elite.nearest.neighbor.min
8.666667e-01
landmarking.elite.nearest.neighbor.median
1.000000e+00
landmarking.elite.nearest.neighbor.max
1.000000e+00
landmarking.linear.discriminant.min
6.000000e-01
landmarking.linear.discriminant.median
9.333333e-01
landmarking.linear.discriminant.max
1.000000e+00
landmarking.naive.bayes.min
7.333333e-01
landmarking.naive.bayes.median
9.666667e-01
landmarking.naive.bayes.max
1.000000e+00
landmarking.nearest.neighbor.min
8.666667e-01
landmarking.nearest.neighbor.median
1.000000e+00
landmarking.nearest.neighbor.max
1.000000e+00
landmarking.worst.node.min
5.333333e-01
landmarking.worst.node.median
8.000000e-01
landmarking.worst.node.max
1.000000e+00
model.based.average.leaf.corrobation.min
3.066667e-01
model.based.average.leaf.corrobation.median
3.333333e-01
model.based.average.leaf.corrobation.max
3.600000e-01
model.based.branch.length.min
1.000000e+00
model.based.branch.length.median
2.000000e+00
model.based.branch.length.max
2.000000e+00
model.based.depth.min
0.000000e+00
model.based.depth.median
1.000000e+00
model.based.depth.max
2.000000e+00
model.based.homogeneity.min
6.000000e+00
model.based.homogeneity.median
6.000000e+00
model.based.homogeneity.max
6.000000e+00
model.based.max.depth
2.000000e+00
model.based.nleave
3.000000e+00
model.based.nnode
2.000000e+00
model.based.nodes.per.attribute
5.000000e-01
model.based.nodes.per.instance
1.333333e-02
model.based.nodes.per.level.min
1.000000e+00
model.based.nodes.per.level.median
1.000000e+00
model.based.nodes.per.level.max
1.000000e+00
model.based.repeated.nodes.min
0.000000e+00
model.based.repeated.nodes.median
5.000000e-01
model.based.repeated.nodes.max
1.000000e+00
model.based.shape.min
5.000000e-01
model.based.shape.median
5.000000e-01
model.based.shape.max
5.000000e-01
model.based.variable.importance.min
0.000000e+00
model.based.variable.importance.median
1.948470e+01
model.based.variable.importance.max
5.000000e+01
statistical.correlation.min
1.777000e-01
statistical.correlation.median
4.915693e-01
statistical.correlation.max
8.642247e-01
statistical.covariance.min
6.069388e-03
statistical.covariance.median
5.243673e-02
statistical.covariance.max
3.032898e-01
statistical.discreteness.degree.min
1.000000e+00
statistical.discreteness.degree.median
1.000000e+00
statistical.discreteness.degree.max
1.000000e+00
statistical.geometric.mean.min
2.265819e-01
statistical.geometric.mean.median
3.182047e+00
statistical.geometric.mean.max
6.557795e+00
statistical.harmonic.mean.min
1.000000e-01
statistical.harmonic.mean.median
3.200000e+00
statistical.harmonic.mean.max
7.900000e+00
statistical.iqr.min
9.488963e-01
statistical.iqr.median
1.264961e+00
statistical.iqr.max
1.820498e+00
statistical.kurtosis.min
1.902555e-01
statistical.kurtosis.median
5.683174e-01
statistical.kurtosis.max
1.258718e+00
statistical.mad.min
0.000000e+00
statistical.mad.median
2.965200e-01
statistical.mad.max
6.671700e-01
statistical.normality.min
0.000000e+00
statistical.normality.median
1.000000e+00
statistical.normality.max
1.000000e+00
statistical.outliers.min
0.000000e+00
statistical.outliers.median
0.000000e+00
statistical.outliers.max
0.000000e+00
statistical.skewness.min
2.933377e-02
statistical.skewness.median
1.173949e-01
statistical.skewness.max
1.179633e+00
statistical.standard.deviation.min
1.053856e-01
statistical.standard.deviation.median
3.374932e-01
statistical.standard.deviation.max
6.358796e-01
statistical.trim.mean.min
2.200000e-01
statistical.trim.mean.median
3.186667e+00
statistical.trim.mean.max
6.546667e+00
statistical.variance.min
1.110612e-02
statistical.variance.median
1.141265e-01
statistical.variance.max
4.043429e-01
mfe documentation built on July 1, 2020, 10:46 p.m.
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Description Usage Arguments Details Value Examples
Description
This is a simple way to extract the meta-features from a dataset, where all meta-features from each group is extracted.
Usage
1 2 3 4 5 6 7 | metafeatures(...)
## Default S3 method:
metafeatures(x, y, groups = "default", summary = c("mean", "sd"), ...)
## S3 method for class 'formula'
metafeatures(formula, data, groups = "default", summary = c("mean", "sd"), ...)
|
Arguments
... |
Optional arguments to the summary methods. |
x |
A data.frame contained only the input attributes. |
y |
A factor response vector with one label for each row/component of x. |
groups |
A list of meta-features groups, |
summary |
A list of summarization functions or empty for all values. See
post.processing method to more information. (Default:
|
formula |
A formula to define the class column. |
data |
A data.frame dataset contained the input attributes and class The details section describes the valid values for this group. |
Details
The following groups are allowed for this method:
- "infotheo"
Include all information theoretical meta-features. See infotheo for more details.
- "general"
Include all general (simple) meta-features. See general for more details.
- "landmarking"
Include all landmarking meta-features. See landmarking for more details.
- "model.based"
Include all model based meta-features. See model.based for more details.
- "statistical"
Include all statistical meta-features. See statistical for more details.
- "clustering"
Include all clustering meta-features. See clustering for more details.
- "complexity"
Include all complexity meta-features. See complexity for more details.
- "concept"
Include all concept variation meta-features. See concept for more details.
- "itemset"
Include all itemset meta-features. See itemset for more details.
Value
A numeric vector named by the meta-features from the specified groups.
Examples
1 2 3 4 5 6 7 8 | ## Extract all meta-features
metafeatures(Species ~ ., iris)
## Extract some groups of meta-features
metafeatures(iris[1:4], iris[5], c("general", "statistical", "infotheo"))
## Use another summary methods
metafeatures(Species ~ ., iris, summary=c("min", "median", "max"))
|
Example output
discriminant.cancor
0.98482089
discriminant.cancor.fract
0.81372024
discriminant.center.of.gravity
3.20828116
discriminant.discfct
0.66666667
discriminant.eigen.fract
0.92461872
discriminant.max.eigenvalue
4.22824171
discriminant.min.eighenvalue
0.02383509
discriminant.sdratio
1.27722888
discriminant.wlambda
0.34245841
general.defective.instances
0.00000000
general.dimensionality
0.02666667
general.majority.class
0.33333333
general.missing.values
0.00000000
general.nattribute
4.00000000
general.nbinary
0.00000000
general.nclasse
3.00000000
general.ninstance
150.00000000
general.nnumeric
4.00000000
general.nsymbolic
0.00000000
general.pbinary
0.00000000
general.pnumeric
1.00000000
general.psymbolic
0.00000000
general.sdclass
0.00000000
infotheo.attributes.concentration.mean
0.20980486
infotheo.attributes.concentration.sd
0.11958799
infotheo.attribute.entropy.mean
0.98073290
infotheo.attribute.entropy.sd
0.02628825
infotheo.class.concentration.mean
0.51481787
infotheo.class.concentration.sd
0.25900418
infotheo.class.entropy
1.00000000
infotheo.equivalent.attributes
1.87806411
infotheo.joint.entropy.mean
3.01821959
infotheo.joint.entropy.sd
0.38218827
infotheo.mutual.information.mean
0.84393418
infotheo.mutual.information.sd
0.42220265
infotheo.noise.signal
1.69830435
landmarking.decision.stumps.mean
0.94444444
landmarking.decision.stumps.sd
0.06085806
landmarking.elite.nearest.neighbor.mean
0.96666667
landmarking.elite.nearest.neighbor.sd
0.04548588
landmarking.linear.discriminant.mean
0.88888889
landmarking.linear.discriminant.sd
0.12904997
landmarking.naive.bayes.mean
0.94222222
landmarking.naive.bayes.sd
0.07161697
landmarking.nearest.neighbor.mean
0.97333333
landmarking.nearest.neighbor.sd
0.04143036
landmarking.worst.node.mean
0.82222222
landmarking.worst.node.sd
0.16913875
model.based.average.leaf.corrobation.mean
0.33333333
model.based.average.leaf.corrobation.sd
0.02666667
model.based.branch.length.mean
1.66666667
model.based.branch.length.sd
0.57735027
model.based.depth.mean
1.20000000
model.based.depth.sd
0.83666003
model.based.homogeneity.mean
6.00000000
model.based.homogeneity.sd
0.00000000
model.based.max.depth
2.00000000
model.based.nleave
3.00000000
model.based.nnode
2.00000000
model.based.nodes.per.attribute
0.50000000
model.based.nodes.per.instance
0.01333333
model.based.nodes.per.level.mean
1.00000000
model.based.nodes.per.level.sd
0.00000000
model.based.repeated.nodes.mean
0.50000000
model.based.repeated.nodes.sd
0.57735027
model.based.shape.mean
0.50000000
model.based.shape.sd
0.00000000
model.based.variable.importance.mean
22.24235105
model.based.variable.importance.sd
26.07505668
statistical.correlation.mean
0.48505297
statistical.correlation.sd
0.20939015
statistical.covariance.mean
0.07154263
statistical.covariance.sd
0.07130389
statistical.discreteness.degree.mean
1.00000000
statistical.discreteness.degree.sd
0.00000000
statistical.geometric.mean.mean
3.44476412
statistical.geometric.mean.sd
2.01825110
statistical.harmonic.mean.mean
3.46450000
statistical.harmonic.mean.sd
1.97548999
statistical.iqr.mean
1.28810588
statistical.iqr.sd
0.25053916
statistical.kurtosis.mean
0.55691608
statistical.kurtosis.sd
0.28615735
statistical.mad.mean
0.35211750
statistical.mad.sd
0.19259539
statistical.normality.mean
0.66666667
statistical.normality.sd
0.57735027
statistical.outliers.mean
0.00000000
statistical.outliers.sd
0.00000000
statistical.skewness.mean
0.29715985
statistical.skewness.sd
0.33328611
statistical.standard.deviation.mean
0.35776315
statistical.standard.deviation.sd
0.16137542
statistical.trim.mean.mean
3.45583333
statistical.trim.mean.sd
2.01128389
statistical.variance.mean
0.15186633
statistical.variance.sd
0.12214091
general.defective.instances general.dimensionality
0.00000000 0.02666667
general.majority.class general.missing.values
0.33333333 0.00000000
general.nattribute general.nbinary
4.00000000 0.00000000
general.nclasse general.ninstance
3.00000000 150.00000000
general.nnumeric general.nsymbolic
4.00000000 0.00000000
general.pbinary general.pnumeric
0.00000000 1.00000000
general.psymbolic general.sdclass
0.00000000 0.00000000
statistical.correlation.mean statistical.correlation.sd
0.48505297 0.20939015
statistical.covariance.mean statistical.covariance.sd
0.07154263 0.07130389
statistical.discreteness.degree.mean statistical.discreteness.degree.sd
1.00000000 0.00000000
statistical.geometric.mean.mean statistical.geometric.mean.sd
3.44476412 2.01825110
statistical.harmonic.mean.mean statistical.harmonic.mean.sd
3.46450000 1.97548999
statistical.iqr.mean statistical.iqr.sd
1.28810588 0.25053916
statistical.kurtosis.mean statistical.kurtosis.sd
0.55691608 0.28615735
statistical.mad.mean statistical.mad.sd
0.35211750 0.19259539
statistical.normality.mean statistical.normality.sd
0.66666667 0.57735027
statistical.outliers.mean statistical.outliers.sd
0.00000000 0.00000000
statistical.skewness.mean statistical.skewness.sd
0.29715985 0.33328611
statistical.standard.deviation.mean statistical.standard.deviation.sd
0.35776315 0.16137542
statistical.trim.mean.mean statistical.trim.mean.sd
3.45583333 2.01128389
statistical.variance.mean statistical.variance.sd
0.15186633 0.12214091
infotheo.attributes.concentration.mean infotheo.attributes.concentration.sd
0.20980486 0.11958799
infotheo.attribute.entropy.mean infotheo.attribute.entropy.sd
0.98073290 0.02628825
infotheo.class.concentration.mean infotheo.class.concentration.sd
0.51481787 0.25900418
infotheo.class.entropy infotheo.equivalent.attributes
1.00000000 1.87806411
infotheo.joint.entropy.mean infotheo.joint.entropy.sd
3.01821959 0.38218827
infotheo.mutual.information.mean infotheo.mutual.information.sd
0.84393418 0.42220265
infotheo.noise.signal
1.69830435
discriminant.cancor
9.848209e-01
discriminant.cancor.fract
8.137202e-01
discriminant.center.of.gravity
3.208281e+00
discriminant.discfct
6.666667e-01
discriminant.eigen.fract
9.246187e-01
discriminant.max.eigenvalue
4.228242e+00
discriminant.min.eighenvalue
2.383509e-02
discriminant.sdratio
1.277229e+00
discriminant.wlambda
3.424584e-01
general.defective.instances
0.000000e+00
general.dimensionality
2.666667e-02
general.majority.class
3.333333e-01
general.missing.values
0.000000e+00
general.nattribute
4.000000e+00
general.nbinary
0.000000e+00
general.nclasse
3.000000e+00
general.ninstance
1.500000e+02
general.nnumeric
4.000000e+00
general.nsymbolic
0.000000e+00
general.pbinary
0.000000e+00
general.pnumeric
1.000000e+00
general.psymbolic
0.000000e+00
general.sdclass
0.000000e+00
infotheo.attributes.concentration.min
8.478340e-02
infotheo.attributes.concentration.median
1.846740e-01
infotheo.attributes.concentration.max
4.299568e-01
infotheo.attribute.entropy.min
9.415587e-01
infotheo.attribute.entropy.median
9.920377e-01
infotheo.attribute.entropy.max
9.972975e-01
infotheo.class.concentration.min
2.227820e-01
infotheo.class.concentration.median
5.465452e-01
infotheo.class.concentration.max
7.433990e-01
infotheo.class.entropy
1.000000e+00
infotheo.equivalent.attributes
1.878064e+00
infotheo.joint.entropy.min
2.682002e+00
infotheo.joint.entropy.median
2.990150e+00
infotheo.joint.entropy.max
3.410577e+00
infotheo.mutual.information.min
3.606172e-01
infotheo.mutual.information.median
9.067693e-01
infotheo.mutual.information.max
1.201581e+00
infotheo.noise.signal
1.698304e+00
landmarking.decision.stumps.min
8.000000e-01
landmarking.decision.stumps.median
9.333333e-01
landmarking.decision.stumps.max
1.000000e+00
landmarking.elite.nearest.neighbor.min
8.666667e-01
landmarking.elite.nearest.neighbor.median
1.000000e+00
landmarking.elite.nearest.neighbor.max
1.000000e+00
landmarking.linear.discriminant.min
6.000000e-01
landmarking.linear.discriminant.median
9.333333e-01
landmarking.linear.discriminant.max
1.000000e+00
landmarking.naive.bayes.min
7.333333e-01
landmarking.naive.bayes.median
9.666667e-01
landmarking.naive.bayes.max
1.000000e+00
landmarking.nearest.neighbor.min
8.666667e-01
landmarking.nearest.neighbor.median
1.000000e+00
landmarking.nearest.neighbor.max
1.000000e+00
landmarking.worst.node.min
5.333333e-01
landmarking.worst.node.median
8.000000e-01
landmarking.worst.node.max
1.000000e+00
model.based.average.leaf.corrobation.min
3.066667e-01
model.based.average.leaf.corrobation.median
3.333333e-01
model.based.average.leaf.corrobation.max
3.600000e-01
model.based.branch.length.min
1.000000e+00
model.based.branch.length.median
2.000000e+00
model.based.branch.length.max
2.000000e+00
model.based.depth.min
0.000000e+00
model.based.depth.median
1.000000e+00
model.based.depth.max
2.000000e+00
model.based.homogeneity.min
6.000000e+00
model.based.homogeneity.median
6.000000e+00
model.based.homogeneity.max
6.000000e+00
model.based.max.depth
2.000000e+00
model.based.nleave
3.000000e+00
model.based.nnode
2.000000e+00
model.based.nodes.per.attribute
5.000000e-01
model.based.nodes.per.instance
1.333333e-02
model.based.nodes.per.level.min
1.000000e+00
model.based.nodes.per.level.median
1.000000e+00
model.based.nodes.per.level.max
1.000000e+00
model.based.repeated.nodes.min
0.000000e+00
model.based.repeated.nodes.median
5.000000e-01
model.based.repeated.nodes.max
1.000000e+00
model.based.shape.min
5.000000e-01
model.based.shape.median
5.000000e-01
model.based.shape.max
5.000000e-01
model.based.variable.importance.min
0.000000e+00
model.based.variable.importance.median
1.948470e+01
model.based.variable.importance.max
5.000000e+01
statistical.correlation.min
1.777000e-01
statistical.correlation.median
4.915693e-01
statistical.correlation.max
8.642247e-01
statistical.covariance.min
6.069388e-03
statistical.covariance.median
5.243673e-02
statistical.covariance.max
3.032898e-01
statistical.discreteness.degree.min
1.000000e+00
statistical.discreteness.degree.median
1.000000e+00
statistical.discreteness.degree.max
1.000000e+00
statistical.geometric.mean.min
2.265819e-01
statistical.geometric.mean.median
3.182047e+00
statistical.geometric.mean.max
6.557795e+00
statistical.harmonic.mean.min
1.000000e-01
statistical.harmonic.mean.median
3.200000e+00
statistical.harmonic.mean.max
7.900000e+00
statistical.iqr.min
9.488963e-01
statistical.iqr.median
1.264961e+00
statistical.iqr.max
1.820498e+00
statistical.kurtosis.min
1.902555e-01
statistical.kurtosis.median
5.683174e-01
statistical.kurtosis.max
1.258718e+00
statistical.mad.min
0.000000e+00
statistical.mad.median
2.965200e-01
statistical.mad.max
6.671700e-01
statistical.normality.min
0.000000e+00
statistical.normality.median
1.000000e+00
statistical.normality.max
1.000000e+00
statistical.outliers.min
0.000000e+00
statistical.outliers.median
0.000000e+00
statistical.outliers.max
0.000000e+00
statistical.skewness.min
2.933377e-02
statistical.skewness.median
1.173949e-01
statistical.skewness.max
1.179633e+00
statistical.standard.deviation.min
1.053856e-01
statistical.standard.deviation.median
3.374932e-01
statistical.standard.deviation.max
6.358796e-01
statistical.trim.mean.min
2.200000e-01
statistical.trim.mean.median
3.186667e+00
statistical.trim.mean.max
6.546667e+00
statistical.variance.min
1.110612e-02
statistical.variance.median
1.141265e-01
statistical.variance.max
4.043429e-01
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