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lines changed Original file line number Diff line number Diff line change @@ -183,7 +183,10 @@ class IterativeImputer(_BaseImputer):
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See Also
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--------
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- SimpleImputer : Univariate imputation of missing values.
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+ SimpleImputer : Univariate imputer for completing missing values
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+ with simple strategies.
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+ KNNImputer : Multivariate imputer that estimates missing features using
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+ nearest samples.
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Notes
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-----
@@ -194,6 +197,16 @@ class IterativeImputer(_BaseImputer):
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Features which contain all missing values at :meth:`fit` are discarded upon
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:meth:`transform`.
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+ Using defaults, the imputer scales in :math:`\\ mathcal{O}(knp^3\\ min(n,p))`
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+ where :math:`k` = `max_iter`, :math:`n` the number of samples and
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+ :math:`p` the number of features. It thus becomes prohibitively costly when
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+ the number of features increases. Setting
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+ `n_nearest_features << n_features`, `skip_complete=True` or increasing `tol`
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+ can help to reduce its computational cost.
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+
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+ Depending on the nature of missing values, simple imputers can be
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+ preferable in a prediction context.
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+
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References
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----------
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.. [1] `Stef van Buuren, Karin Groothuis-Oudshoorn (2011). "mice:
Original file line number Diff line number Diff line change @@ -90,10 +90,10 @@ class KNNImputer(_BaseImputer):
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See Also
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--------
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- SimpleImputer : Imputation transformer for completing missing values
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+ SimpleImputer : Univariate imputer for completing missing values
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with simple strategies.
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- IterativeImputer : Multivariate imputer that estimates each feature
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- from all the others.
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+ IterativeImputer : Multivariate imputer that estimates values to impute for
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+ each feature with missing values from all the others.
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References
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