DOC replace e.g. by i.e. in fit_intercept docstring (scikit-learn#14594)

Author: amueller

sklearn/linear_model/base.py

@@ -371,7 +371,7 @@ class LinearRegression(LinearModel, RegressorMixin, MultiOutputMixin):
     fit_intercept : boolean, optional, default True
         whether to calculate the intercept for this model. If set
         to False, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.

sklearn/linear_model/bayes.py

@@ -76,7 +76,7 @@ class BayesianRidge(LinearModel, RegressorMixin):
         The intercept is not treated as a probabilistic parameter
         and thus has no associated variance. If set
         to False, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
 
     normalize : boolean, optional, default False
@@ -425,7 +425,7 @@ class ARDRegression(LinearModel, RegressorMixin):
     fit_intercept : boolean, optional
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
         Default is True.
 
     normalize : boolean, optional, default False

sklearn/linear_model/coordinate_descent.py

@@ -820,7 +820,7 @@ class Lasso(ElasticNet):
     fit_intercept : boolean, optional, default True
         Whether to calculate the intercept for this model. If set
         to False, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -1258,7 +1258,7 @@ class LassoCV(LinearModelCV, RegressorMixin):
     fit_intercept : boolean, default True
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -1430,7 +1430,7 @@ class ElasticNetCV(LinearModelCV, RegressorMixin):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -1635,7 +1635,7 @@ class MultiTaskElasticNet(Lasso):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -1821,7 +1821,7 @@ class MultiTaskLasso(MultiTaskElasticNet):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -1962,7 +1962,7 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -2136,7 +2136,7 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.

sklearn/linear_model/least_angle.py

@@ -768,7 +768,7 @@ class Lars(LinearModel, RegressorMixin, MultiOutputMixin):
     fit_intercept : boolean
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     verbose : boolean or integer, optional
         Sets the verbosity amount
@@ -987,7 +987,7 @@ class LassoLars(Lars):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     verbose : boolean or integer, optional
         Sets the verbosity amount
@@ -1146,7 +1146,7 @@ def _lars_path_residues(X_train, y_train, X_test, y_test, Gram=None,
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     positive : boolean (default=False)
         Restrict coefficients to be >= 0. Be aware that you might want to
@@ -1232,7 +1232,7 @@ class LarsCV(Lars):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     verbose : boolean or integer, optional
         Sets the verbosity amount
@@ -1446,7 +1446,7 @@ class LassoLarsCV(LarsCV):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     verbose : boolean or integer, optional
         Sets the verbosity amount
@@ -1615,7 +1615,7 @@ class LassoLarsIC(LassoLars):
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     verbose : boolean or integer, optional
         Sets the verbosity amount

sklearn/linear_model/omp.py

@@ -556,7 +556,7 @@ class OrthogonalMatchingPursuit(LinearModel, RegressorMixin, MultiOutputMixin):
     fit_intercept : boolean, optional
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default True
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -701,7 +701,7 @@ def _omp_path_residues(X_train, y_train, X_test, y_test, copy=True,
     fit_intercept : boolean
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default True
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -770,7 +770,7 @@ class OrthogonalMatchingPursuitCV(LinearModel, RegressorMixin):
     fit_intercept : boolean, optional
         whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default True
         This parameter is ignored when ``fit_intercept`` is set to False.

sklearn/linear_model/ridge.py

@@ -631,7 +631,7 @@ class Ridge(_BaseRidge, RegressorMixin):
     fit_intercept : bool, default True
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -1601,7 +1601,7 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin):
     fit_intercept : bool, default True
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.
@@ -1714,7 +1714,7 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
     fit_intercept : boolean
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
-        (e.g. data is expected to be already centered).
+        (i.e. data is expected to be centered).
 
     normalize : boolean, optional, default False
         This parameter is ignored when ``fit_intercept`` is set to False.