added examples to docstrings of LinearSVC and LinearSVR
@@ -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
@@ -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
--------
