DOC minor fixes do docstrings, don't document deprecated parameters.

Author: amueller

sklearn/cluster/hierarchical.py

@@ -643,7 +643,7 @@ class AgglomerativeClustering(BaseEstimator, ClusterMixin):
     pooling_func : callable, default=np.mean
         This combines the values of agglomerated features into a single
         value, and should accept an array of shape [M, N] and the keyword
-        argument `axis=1`, and reduce it to an array of size [M].
+        argument ``axis=1``, and reduce it to an array of size [M].
 
     Attributes
     ----------
@@ -702,7 +702,7 @@ def fit(self, X):
                              "work with euclidean distances." %
                              (self.affinity, ))
 
-        if not self.linkage in _TREE_BUILDERS:
+        if self.linkage not in _TREE_BUILDERS:
             raise ValueError("Unknown linkage type %s."
                              "Valid options are %s" % (self.linkage,
                                                        _TREE_BUILDERS.keys()))

sklearn/ensemble/gradient_boosting.py

@@ -220,7 +220,7 @@ def update_terminal_regions(self, tree, X, y, residual, y_pred,
             The sample mask to be used.
         learning_rate : float, default=0.1
             learning rate shrinks the contribution of each tree by 
-            `learning_rate`.
+            ``learning_rate``.
         k : int, default 0
             The index of the estimator being updated.
 

sklearn/feature_selection/rfe.py

@@ -69,7 +69,7 @@ class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin):
         The mask of selected features.
 
     ranking_ : array of shape [n_features]
-        The feature ranking, such that `ranking_[i]` corresponds to the
+        The feature ranking, such that ``ranking_[i]`` corresponds to the
         ranking position of the i-th feature. Selected (i.e., estimated
         best) features are assigned rank 1.
 

sklearn/lda.py

@@ -382,7 +382,7 @@ def _solve_svd(self, X, y, store_covariance=False, tol=1.0e-4):
         rank = np.sum(S > tol * S[0])
         self.scalings_ = np.dot(scalings, V.T[:, :rank])
         coef = np.dot(self.means_ - self.xbar_, self.scalings_)
-        self.intercept_ = (-0.5 * np.sum(coef**2, axis=1)
+        self.intercept_ = (-0.5 * np.sum(coef ** 2, axis=1)
                            + np.log(self.priors_))
         self.coef_ = np.dot(coef, self.scalings_.T)
         self.intercept_ -= np.dot(self.xbar_, self.coef_.T)
@@ -397,12 +397,6 @@ def fit(self, X, y, store_covariance=False, tol=1.0e-4):
 
         y : array, shape (n_samples,)
             Target values.
-
-        store_covariance : bool, optional
-            Additionally compute class covariance matrix (default False).
-
-        tol : float, optional
-            Threshold used for rank estimation.
         """
         if store_covariance:
             warnings.warn("'store_covariance' was moved to the __init__()"

sklearn/linear_model/base.py

@@ -194,7 +194,7 @@ class would be predicted.
         """
         if not hasattr(self, 'coef_') or self.coef_ is None:
             raise NotFittedError("This %(name)s instance is not fitted"
-                                 "yet"%{'name':type(self).__name__})
+                                 "yet" % {'name': type(self).__name__})
 
         X = check_array(X, accept_sparse='csr')
 
@@ -266,7 +266,7 @@ def densify(self):
         self: estimator
         """
         msg = "Estimator, %(name)s, must be fitted before densifying."
-        check_is_fitted(self, "coef_",  msg=msg)
+        check_is_fitted(self, "coef_", msg=msg)
         if sp.issparse(self.coef_):
             self.coef_ = self.coef_.toarray()
         return self
@@ -296,7 +296,7 @@ def sparsify(self):
         self: estimator
         """
         msg = "Estimator, %(name)s, must be fitted before sparsifying."
-        check_is_fitted(self, "coef_",  msg=msg)
+        check_is_fitted(self, "coef_", msg=msg)
         self.coef_ = sp.csr_matrix(self.coef_)
         return self
 
@@ -360,11 +360,6 @@ def fit(self, X, y, n_jobs=1):
         y : numpy array of shape [n_samples, n_targets]
             Target values
 
-        n_jobs : int, optional, default 1
-            The number of jobs to use for the computation.
-            If -1 all CPUs are used. This will only provide speedup for
-            n_targets > 1 and sufficiently large problems.
-
         Returns
         -------
         self : returns an instance of self.