From cc567ee3c8bb321ec5cf01ff89e332d7ddc1985b Mon Sep 17 00:00:00 2001 From: Andreas Mueller Date: Fri, 20 Jan 2012 19:39:35 +0100 Subject: [PATCH] DOC what's new, minor doc improvements --- doc/modules/grid_search.rst | 5 +++++ doc/whats_new.rst | 7 +++++++ sklearn/ensemble/forest.py | 4 ++-- 3 files changed, 14 insertions(+), 2 deletions(-) diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst index c861ad619ac..6421ab62675 100644 --- a/doc/modules/grid_search.rst +++ b/doc/modules/grid_search.rst @@ -78,6 +78,8 @@ model selection: linear_model.LassoLarsIC +.. _out_of_bag: + Out of Bag Estimates -------------------- @@ -91,9 +93,12 @@ This estimate comes "for free" as no addictional data is needed and can be used for model selection. This is currently implemented in the following classes: + .. autosummary:: :toctree: generated/ :template: class.rst ensemble.RandomForestClassifier + ensemble.RandomForestRegressor ensemble.ExtraTreesClassifier + ensemble.ExtraTreesRegressor diff --git a/doc/whats_new.rst b/doc/whats_new.rst index 08410122843..ca82396de2e 100644 --- a/doc/whats_new.rst +++ b/doc/whats_new.rst @@ -5,6 +5,13 @@ 0.11 ==== +Changelog +--------- + + - :ref:`out_of_bag` of generalization error for :ref:`ensemble` + by `Andreas Müller`_. + + API changes summary ------------------- diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py index 2a1187d8c05..57e28dfc562 100644 --- a/sklearn/ensemble/forest.py +++ b/sklearn/ensemble/forest.py @@ -482,7 +482,7 @@ class RandomForestClassifier(ForestClassifier): `oob_score_` : float Score of the training dataset obtained using an out-of-bag estimate. - `oob_decision_function_` : array, shape = [n_samples, n_classes] + `oob_decision_function_` : array, shape = [n_samples, n_classes] Decision function computed with out-of-bag estimate on the training set. @@ -726,7 +726,7 @@ class ExtraTreesClassifier(ForestClassifier): `oob_score_` : float Score of the training dataset obtained using an out-of-bag estimate. - `oob_decision_function_` : array, shape = [n_samples, n_classes] + `oob_decision_function_` : array, shape = [n_samples, n_classes] Decision function computed with out-of-bag estimate on the training set. Notes