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sklearn.preprocessing.data.OneHotEncoder
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sklearn.preprocessing.data.OneHotEncoder
Visibility: public
Uploaded 14-06-2018 by
Matthias Feurer
sklearn==0.18.1 numpy>=1.6.1 scipy>=0.9
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openml-python
python
scikit-learn
sklearn
sklearn_0.18.1
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Automatically created scikit-learn flow.
Parameters
categorical_features
dtype
default: {"oml-python:serialized_object": "type", "value": "np.float64"}
handle_unknown
default: "ignore"
n_values
default: "auto"
sparse
default: true
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Parameter:
none
categorical features
dtype
handle unknown
n values
sparse
Supervised Classification
Supervised Regression
Learning Curve
Supervised Data Stream Classification
Clustering
Machine Learning Challenge
Survival Analysis
Subgroup Discovery
area under roc curve
average cost
binominal test
build cpu time
build memory
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
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mean weighted precision
weighted recall
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os information
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prior class complexity
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root mean prior squared error
root mean squared error
root relative squared error
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usercpu time millis training
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