DOC some attribute documentation (scikit-learn#14459)

Author: amueller

sklearn/cluster/k_means_.py

@@ -853,7 +853,7 @@ class KMeans(BaseEstimator, ClusterMixin, TransformerMixin):
         converging (see ``tol`` and ``max_iter``), these will not be
         consistent with ``labels_``.
 
-    labels_ :
+    labels_ : array, shape (n_samples,)
         Labels of each point
 
     inertia_ : float

sklearn/cluster/spectral.py

@@ -373,7 +373,7 @@ class SpectralClustering(BaseEstimator, ClusterMixin):
         Affinity matrix used for clustering. Available only if after calling
         ``fit``.
 
-    labels_ :
+    labels_ : array, shape (n_samples,)
         Labels of each point
 
     Examples

sklearn/decomposition/factor_analysis.py

@@ -107,6 +107,9 @@ class FactorAnalysis(BaseEstimator, TransformerMixin):
     n_iter_ : int
         Number of iterations run.
 
+    mean_ : array, shape (n_features,)
+        Per-feature empirical mean, estimated from the training set.
+
     Examples
     --------
     >>> from sklearn.datasets import load_digits

sklearn/ensemble/gradient_boosting.py

@@ -2034,6 +2034,9 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
         The collection of fitted sub-estimators. ``loss_.K`` is 1 for binary
         classification, otherwise n_classes.
 
+    classes_ : array of shape = [n_classes]
+        The classes labels.
+
     Notes
     -----
     The features are always randomly permuted at each split. Therefore,

sklearn/linear_model/passive_aggressive.py

@@ -124,6 +124,13 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
         The actual number of iterations to reach the stopping criterion.
         For multiclass fits, it is the maximum over every binary fit.
 
+    classes_ : array of shape = (n_classes,)
+        The unique classes labels.
+
+    t_ : int
+        Number of weight updates performed during training.
+        Same as ``(n_iter_ * n_samples)``.
+
     Examples
     --------
     >>> from sklearn.linear_model import PassiveAggressiveClassifier
@@ -349,6 +356,10 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
     n_iter_ : int
         The actual number of iterations to reach the stopping criterion.
 
+    t_ : int
+        Number of weight updates performed during training.
+        Same as ``(n_iter_ * n_samples)``.
+
     Examples
     --------
     >>> from sklearn.linear_model import PassiveAggressiveRegressor

sklearn/linear_model/perceptron.py

@@ -108,6 +108,13 @@ class Perceptron(BaseSGDClassifier):
         The actual number of iterations to reach the stopping criterion.
         For multiclass fits, it is the maximum over every binary fit.
 
+    classes_ : array of shape = (n_classes,)
+        The unique classes labels.
+
+    t_ : int
+        Number of weight updates performed during training.
+        Same as ``(n_iter_ * n_samples)``.
+
     Notes
     -----
 

sklearn/linear_model/ridge.py

@@ -865,6 +865,9 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
         Actual number of iterations for each target. Available only for
         sag and lsqr solvers. Other solvers will return None.
 
+    classes_ : array of shape = [n_classes]
+        The classes labels.
+
     Examples
     --------
     >>> from sklearn.datasets import load_breast_cancer
@@ -1772,6 +1775,9 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
     alpha_ : float
         Estimated regularization parameter
 
+    classes_ : array of shape = [n_classes]
+        The classes labels.
+
     Examples
     --------
     >>> from sklearn.datasets import load_breast_cancer

sklearn/linear_model/stochastic_gradient.py

@@ -895,6 +895,12 @@ class SGDClassifier(BaseSGDClassifier):
 
     loss_function_ : concrete ``LossFunction``
 
+    classes_ : array of shape = [n_classes]
+
+    t_ : int
+        Number of weight updates performed during training.
+        Same as ``(n_iter_ * n_samples)``.
+
     Examples
     --------
     >>> import numpy as np
@@ -1485,6 +1491,10 @@ class SGDRegressor(BaseSGDRegressor):
     n_iter_ : int
         The actual number of iterations to reach the stopping criterion.
 
+    t_ : int
+        Number of weight updates performed during training.
+        Same as ``(n_iter_ * n_samples)``.
+
     Examples
     --------
     >>> import numpy as np

sklearn/manifold/spectral_embedding_.py

@@ -396,6 +396,9 @@ class SpectralEmbedding(BaseEstimator):
     affinity_matrix_ : array, shape = (n_samples, n_samples)
         Affinity_matrix constructed from samples or precomputed.
 
+    n_neighbors_ : int
+        Number of nearest neighbors effectively used.
+
     Examples
     --------
     >>> from sklearn.datasets import load_digits

sklearn/naive_bayes.py

@@ -145,6 +145,9 @@ class labels known to the classifier
     epsilon_ : float
         absolute additive value to variances
 
+    classes_ : array-like, shape (n_classes,)
+        Unique class labels.
+
     Examples
     --------
     >>> import numpy as np
@@ -690,6 +693,9 @@ class MultinomialNB(BaseDiscreteNB):
         during fitting. This value is weighted by the sample weight when
         provided.
 
+    classes_ : array-like, shape (n_classes,)
+        Unique class labels.
+
     Examples
     --------
     >>> import numpy as np
@@ -794,6 +800,9 @@ class ComplementNB(BaseDiscreteNB):
         Number of samples encountered for each feature during fitting. This
         value is weighted by the sample weight when provided.
 
+    classes_ : array of shape = [n_classes]
+        The classes labels.
+
     Examples
     --------
     >>> import numpy as np
@@ -899,6 +908,10 @@ class BernoulliNB(BaseDiscreteNB):
         during fitting. This value is weighted by the sample weight when
         provided.
 
+    classes_ : array of shape = [n_classes]
+        The classes labels.
+
+
     Examples
     --------
     >>> import numpy as np