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DOC Avoid training/predicting on random dataset in the tutorial #13234

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DOC Avoid training/predicting on random dataset in the tutorial #13234

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DOC Avoid training/predicting on random dataset in the tutorial
qinhanmin2014 Feb 24, 2019
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Merge remote-tracking branch 'upstream/master' into tutorial
qinhanmin2014 Mar 4, 2019
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15 changes: 6 additions & 9 deletions doc/tutorial/basic/tutorial.rst
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Expand Up @@ -322,29 +322,26 @@ via the :term:`set_params()<set_params>` method. Calling ``fit()`` more than
once will overwrite what was learned by any previous ``fit()``::

>>> import numpy as np
>>> from sklearn.datasets import load_iris
>>> from sklearn.svm import SVC

>>> rng = np.random.RandomState(0)
>>> X = rng.rand(100, 10)
>>> y = rng.binomial(1, 0.5, 100)
>>> X_test = rng.rand(5, 10)
>>> X, y = load_iris(return_X_y=True)

>>> clf = SVC()
>>> clf.set_params(kernel='linear').fit(X, y) # doctest: +NORMALIZE_WHITESPACE
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
kernel='linear', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
>>> clf.predict(X_test)
array([1, 0, 1, 1, 0])
>>> clf.predict(X[:5])
array([0, 0, 0, 0, 0])

>>> clf.set_params(kernel='rbf', gamma='scale').fit(X, y) # doctest: +NORMALIZE_WHITESPACE
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> clf.predict(X_test)
array([0, 0, 0, 1, 0])
>>> clf.predict(X[:5])
array([0, 0, 0, 0, 0])

Here, the default kernel ``rbf`` is first changed to ``linear`` via
:func:`SVC.set_params()<sklearn.svm.SVC.set_params>` after the estimator has
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