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TEST7a22450baesklearn.feature_selection._variance_threshold.VarianceThreshold
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TEST7a22450baesklearn.feature_selection._variance_threshold.VarianceThreshold
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Uploaded 05-11-2024 by
Continuous Integration
sklearn==1.0.2 numpy>=1.14.6 scipy>=1.1.0 joblib>=0.11 threadpoolctl>=2.0.0
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openml-python
python
scikit-learn
sklearn
sklearn_1.0.2
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Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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Supervised Classification
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Learning Curve
Supervised Data Stream Classification
Active Classification
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area under roc curve
average cost
binominal test
build cpu time
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c index
chi-squared
class complexity
class complexity gain
confusion matrix
correlation coefficient
cortana quality
coverage
f measure
information gain
jaccard
kappa
kb relative information score
kohavi wolpert bias squared
kohavi wolpert error
kohavi wolpert sigma squared
kohavi wolpert variance
kononenko bratko information score
matthews correlation coefficient
mean absolute error
mean class complexity
mean class complexity gain
mean f measure
mean kononenko bratko information score
mean precision
mean prior absolute error
mean prior class complexity
mean recall
mean weighted area under roc curve
mean weighted f measure
mean weighted precision
weighted recall
number of instances
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prior class complexity
prior entropy
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root mean prior squared error
root mean squared error
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usercpu time millis
usercpu time millis testing
usercpu time millis training
webb bias
webb error
webb variance
joint entropy
pattern team auroc10
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wall clock time millis training
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