93 lines
3.0 KiB
ReStructuredText
93 lines
3.0 KiB
ReStructuredText
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==========================================================================
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Statistical learning: the setting and the estimator object in scikit-learn
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==========================================================================
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Datasets
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=========
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Scikit-learn deals with learning information from one or more
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datasets that are represented as 2D arrays. They can be understood as a
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list of multi-dimensional observations. We say that the first axis of
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these arrays is the **samples** axis, while the second is the
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**features** axis.
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.. topic:: A simple example shipped with scikit-learn: iris dataset
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::
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>>> from sklearn import datasets
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>>> iris = datasets.load_iris()
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>>> data = iris.data
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>>> data.shape
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(150, 4)
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It is made of 150 observations of irises, each described by 4
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features: their sepal and petal length and width, as detailed in
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``iris.DESCR``.
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When the data is not initially in the ``(n_samples, n_features)`` shape, it
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needs to be preprocessed in order to be used by scikit-learn.
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.. topic:: An example of reshaping data would be the digits dataset
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The digits dataset is made of 1797 8x8 images of hand-written
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digits ::
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>>> digits = datasets.load_digits()
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>>> digits.images.shape
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(1797, 8, 8)
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>>> import matplotlib.pyplot as plt
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>>> plt.imshow(digits.images[-1],
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... cmap=plt.cm.gray_r)
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<...>
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.. image:: /auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
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:target: ../../auto_examples/datasets/plot_digits_last_image.html
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:align: center
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To use this dataset with scikit-learn, we transform each 8x8 image into a
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feature vector of length 64 ::
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>>> data = digits.images.reshape(
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... (digits.images.shape[0], -1)
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... )
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Estimators objects
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===================
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.. Some code to make the doctests run
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>>> from sklearn.base import BaseEstimator
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>>> class Estimator(BaseEstimator):
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... def __init__(self, param1=0, param2=0):
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... self.param1 = param1
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... self.param2 = param2
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... def fit(self, data):
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... pass
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>>> estimator = Estimator()
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**Fitting data**: the main API implemented by scikit-learn is that of the
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`estimator`. An estimator is any object that learns from data;
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it may be a classification, regression or clustering algorithm or
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a *transformer* that extracts/filters useful features from raw data.
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All estimator objects expose a ``fit`` method that takes a dataset
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(usually a 2-d array):
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>>> estimator.fit(data)
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**Estimator parameters**: All the parameters of an estimator can be set
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when it is instantiated or by modifying the corresponding attribute::
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>>> estimator = Estimator(param1=1, param2=2)
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>>> estimator.param1
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1
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**Estimated parameters**: When data is fitted with an estimator,
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parameters are estimated from the data at hand. All the estimated
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parameters are attributes of the estimator object ending by an
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underscore::
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>>> estimator.estimated_param_ #doctest: +SKIP
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