DOC Removes Imputer References from docs
@@ -594,54 +594,7 @@ as a dict, not as scalars.
Imputation of missing values
============================
-For various reasons, many real world datasets contain missing values, often
-encoded as blanks, NaNs or other placeholders. Such datasets however are
-incompatible with scikit-learn estimators which assume that all values in an
-array are numerical, and that all have and hold meaning. A basic strategy to use
-incomplete datasets is to discard entire rows and/or columns containing missing
-values. However, this comes at the price of losing data which may be valuable
-(even though incomplete). A better strategy is to impute the missing values,
-i.e., to infer them from the known part of the data.
-
-The :class:`Imputer` class provides basic strategies for imputing missing
-values, either using the mean, the median or the most frequent value of
-the row or column in which the missing values are located. This class
-also allows for different missing values encodings.
-
-The following snippet demonstrates how to replace missing values,
-encoded as ``np.nan``, using the mean value of the columns (axis 0)
-that contain the missing values::
-
- >>> import numpy as np
- >>> from sklearn.preprocessing import Imputer
- >>> imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
- >>> imp.fit([[1, 2], [np.nan, 3], [7, 6]])
- Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)
- >>> X = [[np.nan, 2], [6, np.nan], [7, 6]]
- >>> print(imp.transform(X)) # doctest: +ELLIPSIS
- [[ 4. 2. ]
- [ 6. 3.666...]
- [ 7. 6. ]]
-
-The :class:`Imputer` class also supports sparse matrices::
-
- >>> import scipy.sparse as sp
- >>> X = sp.csc_matrix([[1, 2], [0, 3], [7, 6]])
- >>> imp = Imputer(missing_values=0, strategy='mean', axis=0)
- >>> imp.fit(X)
- Imputer(axis=0, copy=True, missing_values=0, strategy='mean', verbose=0)
- >>> X_test = sp.csc_matrix([[0, 2], [6, 0], [7, 6]])
- >>> print(imp.transform(X_test)) # doctest: +ELLIPSIS
- [[ 4. 2. ]
- [ 6. 3.666...]
- [ 7. 6. ]]
-
-Note that, here, missing values are encoded by 0 and are thus implicitly stored
-in the matrix. This format is thus suitable when there are many more missing
-values than observed values.
-
-:class:`Imputer` can be used in a Pipeline as a way to build a composite
-estimator that supports imputation. See :ref:`sphx_glr_auto_examples_plot_missing_values.py`.
+Tools for imputing missing values are discussed at :ref:`impute`.
.. _polynomial_features:
