diff --git a/doc/datasets/index.rst b/doc/datasets/index.rst
index 7588fb1d1a34cc91d99e33c8559d1d5ce159674d..9e0e2758d54faa1ff120dacb4134f36c97649a8d 100644
--- a/doc/datasets/index.rst
+++ b/doc/datasets/index.rst
@@ -108,8 +108,8 @@ Sample generators
In addition, scikit-learn includes various random sample generators that
can be used to build artifical datasets of controled size and complexity.
-.. image:: ../auto_examples/images/plot_random_dataset_1.png
- :target: ../auto_examples/plot_random_dataset.html
+.. image:: ../auto_examples/datasets/images/plot_random_dataset_1.png
+ :target: ../auto_examples/datasets/plot_random_dataset.html
:scale: 50
:align: center
diff --git a/doc/modules/pipeline.rst b/doc/modules/pipeline.rst
index 5714d75d0e5f006e0619e186dc21601ee82e2237..97499e641ce23842afd7b18fc8f62646ac0b3c73 100644
--- a/doc/modules/pipeline.rst
+++ b/doc/modules/pipeline.rst
@@ -28,6 +28,7 @@ Usage
The :class:`Pipeline` is build using a list of ``(key, value)`` pairs, where
the ``key`` a string containing the name you want to give this step and ``value``
is an estimator object::
+
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
@@ -40,21 +41,25 @@ is an estimator object::
shrinking=True, tol=0.001, verbose=False))])
The estimators of the pipeline are stored as a list in the ``steps`` attribute::
+
>>> clf.steps[0]
('reduce_dim', PCA(copy=True, n_components=None, whiten=False))
and as a ``dict`` in ``named_steps``::
+
>>> clf.named_steps['reduce_dim']
PCA(copy=True, n_components=None, whiten=False)
Parameters of the estimators in the pipeline can be accessed using the
``<estimator>__<parameter>`` syntax::
+
>>> clf.set_params(svm__C=10) # NORMALIZE_WHITESPACE
Pipeline(steps=[('reduce_dim', PCA(copy=True, n_components=None, whiten=False)), ('svm', SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', probability=False, shrinking=True, tol=0.001,
verbose=False))])
This is particularly important for doing grid searches::
+
>>> from sklearn.grid_search import GridSearchCV
>>> params = dict(reduce_dim__n_components=[2, 5, 10],
... svm__C=[0.1, 10, 100])
diff --git a/doc/presentations.rst b/doc/presentations.rst
index 530226f59be50ef9a3ceb2e770fbb1cbfe6f8e89..b593288a1b7acb495818c9a5a7008aafa7628be1 100644
--- a/doc/presentations.rst
+++ b/doc/presentations.rst
@@ -13,12 +13,14 @@ Videos
- `Introduction to scikit-learn
<http://videolectures.net/icml2010_varaquaux_scik/>`_ by `Gael Varoquaux`_ at
ICML 2010
+
A three minute video from a very early stage of the scikit, explaining the
basic idea and approach we are following.
- `Introduction to statistical learning with scikit
learn <http://archive.org/search.php?query=scikit-learn>`_ by
`Gael Varoquaux`_ at SciPy 2011
+
An extensive tutorial, consisting of four sessions of one hour.
The tutorial covers basics of machine learning,
many algorithms and how to apply them using scikit-learn. The
@@ -36,10 +38,12 @@ Videos
- `Introduction to Interactive Predictive Analytics in Python with scikit-learn <http://www.youtube.com/watch?v=Zd5dfooZWG4>`_
by `Olivier Grisel`_ at PyCon 2012
+
3-hours long introduction to prediction tasks using the scikit-learn.
- `scikit-learn - Machine Learning in Python <http://marakana.com/s/scikit-learn_machine_learning_in_python,1152/index.html>`_
by `Jake Vanderplas`_ at the 2012 PyData workshop at Google
+
Interactive demonstration of some scikit-learn features. 75 minutes.
.. _Gael Varoquaux: http://gael-varoquaux.info
diff --git a/doc/tutorial/basic/tutorial.rst b/doc/tutorial/basic/tutorial.rst
index ba5d294d5fc415ed738f46705aa8be0243e759f3..699df18ec4fa676989a19f218bfe7e1375684b4a 100644
--- a/doc/tutorial/basic/tutorial.rst
+++ b/doc/tutorial/basic/tutorial.rst
@@ -179,8 +179,8 @@ which we have not used to train the classifier::
The corresponding image is the following:
-.. image:: ../../auto_examples/images/datasets/plot_digits_last_image_1.png
- :target: ../../auto_examples/plot_digits_last_image.html
+.. image:: ../../auto_examples/datasets/images/plot_digits_last_image_1.png
+ :target: ../../auto_examples/datasets/plot_digits_last_image.html
:align: center
:scale: 50
diff --git a/doc/tutorial/statistical_inference/settings.rst b/doc/tutorial/statistical_inference/settings.rst
index 6fce0dd462eca221d0fc19cfa8a36ae6fbb58aed..63cca4f010dda22ae6619037f5b50e914fda16da 100644
--- a/doc/tutorial/statistical_inference/settings.rst
+++ b/doc/tutorial/statistical_inference/settings.rst
@@ -31,8 +31,8 @@ needs to be preprocessed to be used by the scikit.
.. topic:: An example of reshaping data: the digits dataset
- .. image:: ../../auto_examples/images/datasets/plot_digits_last_image_1.png
- :target: ../../auto_examples/plot_digits_last_image.html
+ .. image:: ../../auto_examples/datasets/images/plot_digits_last_image_1.png
+ :target: ../../auto_examples/datasets/plot_digits_last_image.html
:align: right
:scale: 60
diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
index 37ca2793b1b05e0e66cb1d595140b5fd69f88faa..0a65b2627877a66dfbde89ae6711dc42eb52086c 100644
--- a/doc/tutorial/statistical_inference/supervised_learning.rst
+++ b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -39,8 +39,8 @@ Nearest neighbor and the curse of dimensionality
.. topic:: Classifying irises:
- .. image:: ../../auto_examples/images/datasets/plot_iris_dataset_1.png
- :target: ../../auto_examples/plot_iris_dataset.html
+ .. image:: ../../auto_examples/datasets/images/plot_iris_dataset_1.png
+ :target: ../../auto_examples/datasets/plot_iris_dataset.html
:align: right
:scale: 65
@@ -546,8 +546,8 @@ creating an decision energy by positioning *kernels* on observations:
`svm_gui.py`; add data points of both classes with right and left button,
fit the model and change parameters and data.
-.. image:: ../../auto_examples/images/datasets/plot_iris_dataset_1.png
- :target: ../../auto_examples/plot_iris_dataset.html
+.. image:: ../../auto_examples/datasets/images/plot_iris_dataset_1.png
+ :target: ../../auto_examples/datasets/plot_iris_dataset.html
:align: right
:scale: 70