class sklearn.impute.MissingIndicator(missing_values=nan, features=’missing-only’, sparse=’auto’, error_on_new=True)
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Binary indicators for missing values.
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>>> import numpy as np >>> from sklearn.impute import MissingIndicator >>> X1 = np.array([[np.nan, 1, 3], ... [4, 0, np.nan], ... [8, 1, 0]]) >>> X2 = np.array([[5, 1, np.nan], ... [np.nan, 2, 3], ... [2, 4, 0]]) >>> indicator = MissingIndicator() >>> indicator.fit(X1) MissingIndicator(error_on_new=True, features='missing-only', missing_values=nan, sparse='auto') >>> X2_tr = indicator.transform(X2) >>> X2_tr array([[False, True], [ True, False], [False, False]])
fit (X[, y]) | Fit the transformer on X. |
fit_transform (X[, y]) | Generate missing values indicator for X. |
get_params ([deep]) | Get parameters for this estimator. |
set_params (**params) | Set the parameters of this estimator. |
transform (X) | Generate missing values indicator for X. |
__init__(missing_values=nan, features=’missing-only’, sparse=’auto’, error_on_new=True)
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fit(X, y=None)
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Fit the transformer on X.
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fit_transform(X, y=None)
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Generate missing values indicator for X.
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get_params(deep=True)
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Get parameters for this estimator.
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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transform(X)
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Generate missing values indicator for X.
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sklearn.impute.MissingIndicator
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.impute.MissingIndicator.html