From 1f388152d4ce8d7dc317a9889d95117252771386 Mon Sep 17 00:00:00 2001 From: Hanmin Qin Date: Sun, 24 Feb 2019 16:10:05 +0800 Subject: [PATCH] DOC Avoid training/predicting on random dataset in the tutorial --- doc/tutorial/basic/tutorial.rst | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/doc/tutorial/basic/tutorial.rst b/doc/tutorial/basic/tutorial.rst index 769fd6ae2d66d..edf2bdeea59b5 100644 --- a/doc/tutorial/basic/tutorial.rst +++ b/doc/tutorial/basic/tutorial.rst @@ -322,12 +322,9 @@ via the :term:`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 @@ -335,16 +332,16 @@ once will overwrite what was learned by any previous ``fit()``:: 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()` after the estimator has