.. _supervised_learning_tut:

=======================================================================================
Supervised learning: predicting an output variable from high-dimensional observations
=======================================================================================


.. topic:: The problem solved in supervised learning

   :ref:`Supervised learning <supervised-learning>`
   consists in learning the link between two
   datasets: the observed data `X` and an external variable `y` that we
   are trying to predict, usually called `target` or `labels`. Most often,
   `y` is a 1D array of length `n_samples`.

   All supervised `estimators <http://en.wikipedia.org/wiki/Estimator>`_
   in the `scikit-learn` implement a `fit(X, y)`
   method to fit the model and a `predict(X)` method that, given
   unlabeled observations `X`, returns the predicted labels `y`.

.. topic:: Vocabulary: classification and regression

   If the prediction task is to classify the observations in a set of
   finite labels, in other words to "name" the objects observed, the task
   is said to be a **classification** task. On the other hand, if the goal
   is to predict a continuous target variable, it is said to be a
   **regression** task.

   In the `scikit-learn` for classification tasks, `y` is a vector of
   integers.

   Note: See the :ref:`Introduction to machine learning with Scikit-learn
   Tutorial <introduction>` for a quick run-through on the basic machine
   learning vocabulary used within Scikit-learn.

Nearest neighbor and the curse of dimensionality
=================================================

.. topic:: Classifying irises:

    .. image:: ../../auto_examples/datasets/images/plot_iris_dataset_001.png
        :target: ../../auto_examples/datasets/plot_iris_dataset.html
        :align: right
	:scale: 65

    The iris dataset is a classification task consisting in identifying 3
    different types of irises (Setosa, Versicolour, and Virginica) from
    their petal and sepal length and width::

        >>> import numpy as np
        >>> from sklearn import datasets
        >>> iris = datasets.load_iris()
        >>> iris_X = iris.data
        >>> iris_y = iris.target
        >>> np.unique(iris_y)
        array([0, 1, 2])

k-Nearest neighbors classifier
-------------------------------

The simplest possible classifier is the
`nearest neighbor <http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm>`_:
given a new observation ``X_test``, find in the training set (i.e. the data
used to train the estimator) the observation with the closest feature vector.
(Please see the :ref:`Nearest Neighbors section<neighbors>` of the online
Scikit-learn documentation for more information about this type of classifier.)

.. topic:: Training set and testing set

   While experimenting with any learning algorithm, it is important not to
   test the prediction of an estimator on the data used to fit the
   estimator as this would not be evaluating the performance of the
   estimator on **new data**. This is why datasets are often split into
   *train* and *test* data.

**KNN (k nearest neighbors) classification example**:

.. image:: ../../auto_examples/neighbors/images/plot_classification_001.png
   :target: ../../auto_examples/neighbors/plot_classification.html
   :align: center
   :scale: 70

::

    >>> # Split iris data in train and test data
    >>> # A random permutation, to split the data randomly
    >>> np.random.seed(0)
    >>> indices = np.random.permutation(len(iris_X))
    >>> iris_X_train = iris_X[indices[:-10]]
    >>> iris_y_train = iris_y[indices[:-10]]
    >>> iris_X_test  = iris_X[indices[-10:]]
    >>> iris_y_test  = iris_y[indices[-10:]]
    >>> # Create and fit a nearest-neighbor classifier
    >>> from sklearn.neighbors import KNeighborsClassifier
    >>> knn = KNeighborsClassifier()
    >>> knn.fit(iris_X_train, iris_y_train)
    KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
               n_neighbors=5, p=2, weights='uniform')
    >>> knn.predict(iris_X_test)
    array([1, 2, 1, 0, 0, 0, 2, 1, 2, 0])
    >>> iris_y_test
    array([1, 1, 1, 0, 0, 0, 2, 1, 2, 0])

.. _curse_of_dimensionality:

The curse of dimensionality
-------------------------------

For an estimator to be effective, you need the distance between neighboring
points to be less than some value `d`, which depends on the problem.
In one dimension, this requires on average `n ~ 1/d` points.
In the context of the above `KNN` example, if the data is described by just
one feature with values ranging from 0 to 1 and with `n` training observations,
then new data will be no further away than `1/n`.
Therefore, the nearest neighbor decision rule will be efficient as soon as
`1/n` is small compared to the scale of between-class feature variations.

If the number of features is `p`, you now require `n ~ 1/d^p` points.
Let's say that we require 10 points in one dimension: Now `10^p` points
are required in `p` dimensions to pave the `[0, 1]` space.
As `p` becomes large, the number of training points required for a good
estimator grows exponentially.

For example, if each point is just a single number (8 bytes), then an
effective `KNN` estimator in a paltry `p~20` dimensions would require more training
data than the current estimated size of the entire internet! (±1000 Exabytes or so).

This is called the
`curse of dimensionality  <http://en.wikipedia.org/wiki/Curse_of_dimensionality>`_
and is a core problem that machine learning addresses.

Linear model: from regression to sparsity
==========================================

.. topic:: Diabetes dataset

    The diabetes dataset consists of 10 physiological variables (age,
    sex, weight, blood pressure) measure on 442 patients, and an
    indication of disease progression after one year::

        >>> diabetes = datasets.load_diabetes()
        >>> diabetes_X_train = diabetes.data[:-20]
        >>> diabetes_X_test  = diabetes.data[-20:]
        >>> diabetes_y_train = diabetes.target[:-20]
        >>> diabetes_y_test  = diabetes.target[-20:]

    The task at hand is to predict disease progression from physiological
    variables.

Linear regression
------------------

.. currentmodule:: sklearn.linear_model

:class:`LinearRegression`,
in it's simplest form, fits a linear model to the data set by adjusting
a set of parameters in order to make the sum of the squared residuals
of the model as small as possible.

.. image:: ../../auto_examples/linear_model/images/plot_ols_001.png
   :target: ../../auto_examples/linear_model/plot_ols.html
   :scale: 40
   :align: right

Linear models: :math:`y = X\beta + \epsilon`

 * :math:`X`: data
 * :math:`y`: target variable
 * :math:`\beta`: Coefficients
 * :math:`\epsilon`: Observation noise

::

    >>> from sklearn import linear_model
    >>> regr = linear_model.LinearRegression()
    >>> regr.fit(diabetes_X_train, diabetes_y_train)
    LinearRegression(copy_X=True, fit_intercept=True, normalize=False)
    >>> print(regr.coef_)
    [   0.30349955 -237.63931533  510.53060544  327.73698041 -814.13170937
      492.81458798  102.84845219  184.60648906  743.51961675   76.09517222]

    >>> # The mean square error
    >>> np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2)# doctest: +ELLIPSIS
    2004.56760268...

    >>> # Explained variance score: 1 is perfect prediction
    >>> # and 0 means that there is no linear relationship
    >>> # between X and Y.
    >>> regr.score(diabetes_X_test, diabetes_y_test) # doctest: +ELLIPSIS
    0.5850753022690...


.. _shrinkage:

Shrinkage
----------

If there are few data points per dimension, noise in the observations
induces high variance:

.. image:: ../../auto_examples/linear_model/images/plot_ols_ridge_variance_001.png
   :target: ../../auto_examples/linear_model/plot_ols_ridge_variance.html
   :scale: 70
   :align: right

::

    >>> X = np.c_[ .5, 1].T
    >>> y = [.5, 1]
    >>> test = np.c_[ 0, 2].T
    >>> regr = linear_model.LinearRegression()

    >>> import pylab as pl # doctest: +SKIP
    >>> pl.figure() # doctest: +SKIP

    >>> np.random.seed(0)
    >>> for _ in range(6): # doctest: +SKIP
    ...    this_X = .1*np.random.normal(size=(2, 1)) + X
    ...    regr.fit(this_X, y)
    ...    pl.plot(test, regr.predict(test)) # doctest: +SKIP
    ...    pl.scatter(this_X, y, s=3)  # doctest: +SKIP



A solution in high-dimensional statistical learning is to *shrink* the
regression coefficients to zero: any two randomly chosen set of
observations are likely to be uncorrelated. This is called :class:`Ridge`
regression:

.. image:: ../../auto_examples/linear_model/images/plot_ols_ridge_variance_002.png
   :target: ../../auto_examples/linear_model/plot_ols_ridge_variance.html
   :scale: 70
   :align: right

::

    >>> regr = linear_model.Ridge(alpha=.1)

    >>> pl.figure() # doctest: +SKIP

    >>> np.random.seed(0)
    >>> for _ in range(6): # doctest: +SKIP
    ...    this_X = .1*np.random.normal(size=(2, 1)) + X
    ...    regr.fit(this_X, y)
    ...    pl.plot(test, regr.predict(test)) # doctest: +SKIP
    ...    pl.scatter(this_X, y, s=3) # doctest: +SKIP

This is an example of **bias/variance tradeoff**: the larger the ridge
`alpha` parameter, the higher the bias and the lower the variance.

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)
    >>> from __future__ import print_function  
    >>> 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...]


.. note::

    Capturing in the fitted parameters noise that prevents the model to
    generalize to new data is called
    `overfitting <http://en.wikipedia.org/wiki/Overfitting>`_. The bias introduced
    by the ridge regression is called a
    `regularization <http://en.wikipedia.org/wiki/Regularization_%28machine_learning%29>`_.

.. _sparsity:

Sparsity
----------


.. |diabetes_ols_1| image:: ../../auto_examples/linear_model/images/plot_ols_3d_001.png
   :target: ../../auto_examples/linear_model/plot_ols_3d.html
   :scale: 65

.. |diabetes_ols_3| image:: ../../auto_examples/linear_model/images/plot_ols_3d_003.png
   :target: ../../auto_examples/linear_model/plot_ols_3d.html
   :scale: 65

.. |diabetes_ols_2| image:: ../../auto_examples/linear_model/images/plot_ols_3d_002.png
   :target: ../../auto_examples/linear_model/plot_ols_3d.html
   :scale: 65




.. rst-class:: centered

    **Fitting only features 1 and 2**

.. centered:: |diabetes_ols_1| |diabetes_ols_3| |diabetes_ols_2|

.. note::

   A representation of the full diabetes dataset would involve 11
   dimensions (10 feature dimensions and one of the target variable). It
   is hard to develop an intuition on such representation, but it may be
   useful to keep in mind that it would be a fairly *empty* space.



We can see that, although feature 2 has a strong coefficient on the full
model, it conveys little information on `y` when considered with feature
1.

To improve the conditioning of the problem (i.e. mitigating the
:ref:`curse_of_dimensionality`), it would be interesting to select only the
informative features and set non-informative ones, like feature 2 to 0. Ridge
regression will decrease their contribution, but not set them to zero. Another
penalization approach, called :ref:`lasso` (least absolute shrinkage and
selection operator), can set some coefficients to zero. Such methods are
called **sparse method** and sparsity can be seen as an
application of Occam's razor: `prefer simpler models`.

::

    >>> regr = linear_model.Lasso()
    >>> scores = [regr.set_params(alpha=alpha
    ...             ).fit(diabetes_X_train, diabetes_y_train
    ...             ).score(diabetes_X_test, diabetes_y_test)
    ...        for alpha in alphas]
    >>> best_alpha = alphas[scores.index(max(scores))]
    >>> regr.alpha = best_alpha
    >>> regr.fit(diabetes_X_train, diabetes_y_train)
    Lasso(alpha=0.025118864315095794, copy_X=True, fit_intercept=True,
       max_iter=1000, normalize=False, positive=False, precompute='auto',
       tol=0.0001, warm_start=False)
    >>> print(regr.coef_)
    [   0.         -212.43764548  517.19478111  313.77959962 -160.8303982    -0.
     -187.19554705   69.38229038  508.66011217   71.84239008]

.. topic:: **Different algorithms for the same problem**

    Different algorithms can be used to solve the same mathematical
    problem. For instance the `Lasso` object in the `scikit-learn`
    solves the lasso regression problem using a
    `coordinate decent <http://en.wikipedia.org/wiki/Coordinate_descent>`_ method,
    that is efficient on large datasets. However, the `scikit-learn` also
    provides the :class:`LassoLars` object using the *LARS* which is very
    efficient for problems in which the weight vector estimated is very
    sparse, (i.e. problems with very few observations).

.. _clf_tut:

Classification
---------------

.. image:: ../../auto_examples/linear_model/images/plot_logistic_001.png
   :target: ../../auto_examples/linear_model/plot_logistic.html
   :scale: 65
   :align: right

For classification, as in the labeling
`iris <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ task, linear
regression is not the right approach as it will give too much weight to
data far from the decision frontier. A linear approach is to fit a sigmoid
function or **logistic** function:

.. math::

   y = \textrm{sigmoid}(X\beta - \textrm{offset}) + \epsilon =
   \frac{1}{1 + \textrm{exp}(- X\beta + \textrm{offset})} + \epsilon

::

    >>> logistic = linear_model.LogisticRegression(C=1e5)
    >>> logistic.fit(iris_X_train, iris_y_train)
    LogisticRegression(C=100000.0, class_weight=None, dual=False,
              fit_intercept=True, intercept_scaling=1, penalty='l2',
              random_state=None, tol=0.0001)

This is known as :class:`LogisticRegression`.

.. image:: ../../auto_examples/linear_model/images/plot_iris_logistic_001.png
   :target: ../../auto_examples/linear_model/plot_iris_logistic.html
   :scale: 83

.. topic:: Multiclass classification

   If you have several classes to predict, an option often used is to fit
   one-versus-all classifiers and then use a voting heuristic for the final
   decision.

.. topic:: Shrinkage and sparsity with logistic regression

   The `C` parameter controls the amount of regularization in the
   :class:`LogisticRegression` object: a large value for `C` results in
   less regularization.
   `penalty="l2"` gives :ref:`shrinkage` (i.e. non-sparse coefficients), while
   `penalty="l1"` gives :ref:`sparsity`.

.. topic:: **Exercise**
   :class: green

   Try classifying the digits dataset with nearest neighbors and a linear
   model. Leave out the last 10% and test prediction performance on these
   observations.

   .. literalinclude:: ../../auto_examples/exercises/plot_digits_classification_exercise.py
       :lines: 12-17

   Solution: :download:`../../auto_examples/exercises/plot_digits_classification_exercise.py`


Support vector machines (SVMs)
================================

Linear SVMs
-------------


:ref:`svm` belong to the discriminant model family: they try to find a combination of
samples to build a plane maximizing the margin between the two classes.
Regularization is set by the `C` parameter: a small value for `C` means the margin
is calculated using many or all of the observations around the separating line
(more regularization);
a large value for `C` means the margin is calculated on observations close to
the separating line (less regularization).

.. currentmodule :: sklearn.svm

.. |svm_margin_unreg| image:: ../../auto_examples/svm/images/plot_svm_margin_001.png
   :target: ../../auto_examples/svm/plot_svm_margin.html
   :scale: 70

.. |svm_margin_reg| image:: ../../auto_examples/svm/images/plot_svm_margin_002.png
   :target: ../../auto_examples/svm/plot_svm_margin.html
   :scale: 70

.. rst-class:: centered

    ============================= ==============================
     **Unregularized SVM**         **Regularized SVM (default)**
    ============================= ==============================
    |svm_margin_unreg|  	  |svm_margin_reg|
    ============================= ==============================

.. topic:: Example:

 - :ref:`example_svm_plot_iris.py`


SVMs can be used in regression --:class:`SVR` (Support Vector Regression)--, or in
classification --:class:`SVC` (Support Vector Classification).

::

    >>> from sklearn import svm
    >>> svc = svm.SVC(kernel='linear')
    >>> svc.fit(iris_X_train, iris_y_train)    # doctest: +NORMALIZE_WHITESPACE
    SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
      kernel='linear', max_iter=-1, probability=False, random_state=None,
      shrinking=True, tol=0.001, verbose=False)


.. warning:: **Normalizing data**

   For many estimators, including the SVMs, having datasets with unit
   standard deviation for each feature is important to get good
   prediction.

.. _using_kernels_tut:

Using kernels
--------------

Classes are not always linearly separable in feature space. The solution is to
build a decision function that is not linear but may be polynomial instead.
This is done using the *kernel trick* that can be seen as
creating a decision energy by positioning *kernels* on observations:

.. |svm_kernel_linear| image:: ../../auto_examples/svm/images/plot_svm_kernels_001.png
   :target: ../../auto_examples/svm/plot_svm_kernels.html
   :scale: 65

.. |svm_kernel_poly| image:: ../../auto_examples/svm/images/plot_svm_kernels_002.png
   :target: ../../auto_examples/svm/plot_svm_kernels.html
   :scale: 65

.. rst-class:: centered

  .. list-table::

     *

       - **Linear kernel**

       - **Polynomial kernel**



     *

       - |svm_kernel_linear|

       - |svm_kernel_poly|



     *

       - ::

            >>> svc = svm.SVC(kernel='linear')

       - ::

            >>> svc = svm.SVC(kernel='poly',
            ...               degree=3)
            >>> # degree: polynomial degree



.. |svm_kernel_rbf| image:: ../../auto_examples/svm/images/plot_svm_kernels_003.png
   :target: ../../auto_examples/svm/plot_svm_kernels.html
   :scale: 65

.. rst-class:: centered

  .. list-table::

     *

       - **RBF kernel (Radial Basis Function)**


     *

       - |svm_kernel_rbf|

     *

       - ::

            >>> svc = svm.SVC(kernel='rbf')
            >>> # gamma: inverse of size of
            >>> # radial kernel



.. topic:: **Interactive example**

   See the :ref:`SVM GUI <example_applications_svm_gui.py>` to download
   `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/datasets/images/plot_iris_dataset_001.png
    :target: ../../auto_examples/datasets/plot_iris_dataset.html
    :align: right
    :scale: 70

.. topic:: **Exercise**
   :class: green

   Try classifying classes 1 and 2 from the iris dataset with SVMs, with
   the 2 first features. Leave out 10% of each class and test prediction
   performance on these observations.

   **Warning**: the classes are ordered, do not leave out the last 10%,
   you would be testing on only one class.

   **Hint**: You can use the `decision_function` method on a grid to get
   intuitions.

   .. literalinclude:: ../../auto_examples/exercises/plot_iris_exercise.py
       :lines: 15-22

   Solution: :download:`../../auto_examples/exercises/plot_iris_exercise.py`


