settings.rst

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Statistical learning: the setting and the estimator object in the scikit-learn
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Datasets
=========

The `scikit-learn` deals with learning information from one or more
datasets that are represented as 2D arrays. They can be understood as a
list of multi-dimensional observations. We say that the first axis of
these arrays is the **samples** axis, while the second is the
**features** axis.

.. topic:: A simple example shipped with the scikit: iris dataset

    ::

        >>> from scikits.learn import datasets
        >>> iris = datasets.load_iris()
        >>> data = iris.data
        >>> data.shape
        (150, 4)

    It is made of 150 observations of irises, each described by 4
    features: their sepal and petal length and width, as detailed in
    `iris.DESCR <https://raw.github.com/GaelVaroquaux/scikit-learn/
    stat_tutorial/sklearn/datasets/descr/iris.rst>`_.

When the data is not intially in the `(n_samples, n_features)` shape, it
needs to be preprocessed to be used by the scikit.

.. topic:: An example of reshaping data: the digits dataset 

    .. image:: ../../auto_examples/tutorial/images/plot_digits_first_image_1.png
        :align: right
        :scale: 50

    The digits dataset is made of 1797 8x8 images of hand-written
    digits ::

        >>> digits = datasets.load_digits()
        >>> digits.images.shape
        (1797, 8, 8)
        >>> import pylab as pl
        >>> pl.imshow(digits.images[0], cmap=pl.cm.gray_r) #doctest: +ELLIPSIS
        <matplotlib.image.AxesImage object at ...>

    To use this dataset with the scikit, we transform each 8x8 image in a
    feature vector of length 64 ::

        >>> data = digits.images.reshape((digits.images.shape[0], -1))


Estimators objects
===================

.. Some code to make the doctests run

   >>> from scikits.learn.base import BaseEstimator
   >>> class Estimator(BaseEstimator):
   ...      def __init__(self, param1=0, param2=0):
   ...          self.param1 = param1
   ...          self.param2 = param2
   ...      def fit(self, data):
   ...          pass
   >>> estimator = Estimator()

**Fitting data**: The core object of the `scikit-learn` is the
`estimator` object. All estimator objects expose a `fit` method, that
takes a dataset (2D array)::

    >>> estimator.fit(data)

**Estimator parameters**: All the parameters of an estimator can be set
when it is instanciated, or by modifying the corresponding attribute::

    >>> estimator = Estimator(param1=1, param2=2)
    >>> estimator.param1
    1

**Estimated parameters**: When data is fitted with an estimator,
parameters are estimated from the data at hand. All the estimated
parameters are attributes of the estimator object ending by an
underscore::

    >>> estimator.estimated_param_ #doctest: +SKIP