MISC don't use private ``set_params`` method as that raises a warning.

doc/tutorial/statistical_inference/supervised_learning.rst

@@ -253,7 +253,7 @@ We can choose `alpha` to minimize left out error, this time using the
 diabetes dataset, rather than our synthetic data:: 
 
     >>> alphas = np.logspace(-4, -1, 6)
-    >>> print [regr._set_params(alpha=alpha 
+    >>> print [regr.set_params(alpha=alpha
     ...             ).fit(diabetes_X_train, diabetes_y_train,
     ...             ).score(diabetes_X_test, diabetes_y_test) for alpha in alphas] # doctest: +ELLIPSIS
     [0.5851110683883..., 0.5852073015444..., 0.5854677540698..., 0.5855512036503..., 0.5830717085554..., 0.57058999437...]
@@ -319,7 +319,7 @@ application of Occam's razor: `prefer simpler models`.
 :: 
 
     >>> regr = linear_model.Lasso(alpha=.1)
-    >>> print [regr._set_params(alpha=alpha
+    >>> print [regr.set_params(alpha=alpha
     ...             ).fit(diabetes_X_train, diabetes_y_train
     ...             ).score(diabetes_X_test, diabetes_y_test) 
     ...        for alpha in alphas] # doctest: +ELLIPSIS