DOC Fix typos in doc/developers/contributing.rst
@@ -1460,7 +1460,7 @@ of estimators that allow programmatic inspection of their capabilities, such as
sparse matrix support, supported output types and supported methods. The
estimator tags are a dictionary returned by the method ``_get_tags()``. These
tags are used by the common tests and the :func:`sklearn.utils.estomator_checks.check_estimator` function to
-decide what tests to run and what input data is appropriate. Tags can depends on
+decide what tests to run and what input data is appropriate. Tags can depend on
estimator parameters or even system architecture and can in general only be
determined at runtime.
@@ -1509,13 +1509,13 @@ X_types
in the list, signifying that the estimator takes continuous 2d numpy arrays as input. The default
value is ['2darray']. Other possible types are ``'string'``, ``'sparse'``,
``'categorical'``, ``dict``, ``'1dlabels'`` and ``'2dlabels'``.
- The goals is that in the future the supported input type will determine the
- data used during testsing, in particular for ``'string'``, ``'sparse'`` and
+ The goal is that in the future the supported input type will determine the
+ data used during testing, in particular for ``'string'``, ``'sparse'`` and
``'categorical'`` data. For now, the test for sparse data do not make use
of the ``'sparse'`` tag.
-In addition to the tags, estimators are also need to declare any non-optional
+In addition to the tags, estimators also need to declare any non-optional
parameters to ``__init__`` in the ``_required_parameters`` class attribute,
which is a list or tuple. If ``_required_parameters`` is only
``["estimator"]`` or ``["base_estimator"]``, then the estimator will be
