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Added examples to docstrings of LinearSVC and LinearSVR #9375

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Jul 16, 2017
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Added examples to docstrings of LinearSVC and LinearSVR #9375

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34 changes: 34 additions & 0 deletions sklearn/svm/classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,6 +108,24 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.

Examples
--------
>>> from sklearn.svm import LinearSVC
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = LinearSVC(random_state=0)
>>> clf.fit(X, y)
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l2', random_state=0, tol=0.0001,
verbose=0)
>>> print(clf.coef_)
[[ 0.08551385 0.39414796 0.49847831 0.37513797]]
>>> print(clf.intercept_)
[ 0.28418066]
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]

Notes
-----
The underlying C implementation uses a random number generator to
Expand Down Expand Up @@ -302,6 +320,22 @@ class LinearSVR(LinearModel, RegressorMixin):
intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.

Examples
--------
>>> from sklearn.svm import LinearSVR
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = LinearSVR(random_state=0)
>>> regr.fit(X, y)
LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,
intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,
random_state=0, tol=0.0001, verbose=0)
>>> print(regr.coef_)
[ 16.35750999 26.91499923 42.30652207 60.47843124]
>>> print(regr.intercept_)
[-4.29756543]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-4.29756543]

See also
--------
Expand Down