class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1) [source] Nu Support Vector Regression. Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-SVR. The implementation is based on libsvm. Read more in the User Guide. Parameters: C : float, optional (

class sklearn.svm.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None, random_state=None) [source] Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. The implementation is based on libsvm. Read more in the User Guide. Parameters: nu : float, optional (default=0.5) An upper bound on

class sklearn.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000) [source] Linear Support Vector Regression. Similar to SVR with parameter kernel=?linear?, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples. This class supports both dense and

class sklearn.svm.LinearSVC(penalty='l2', loss='squared_hinge', dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000) [source] Linear Support Vector Classification. Similar to SVC with parameter kernel=?linear?, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.

Three different types of SVM-Kernels are displayed below. The polynomial and RBF are especially useful when the data-points are not linearly separable. print(__doc__) # Code source: Ga Varoquaux # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn import svm # Our dataset and targets X = np.c_[(.4, -.7), (-1.5, -1), (-1.4, -.9), (-1.3, -1.2), (-1.1, -.2), (-1.2, -.4), (-.5, 1.2),

This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores. print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets, feature_selection from sklearn.model_selection import cross_val_score from sklearn.pipeline import Pipeline Import some data to play with digits = datasets.load_digits() y = digits.target # Throw away data, to be in the curse of dimension settin

Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors. print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim dataset Y = iris.target def my_kernel(X, Y): """ We create a cus

The plots below illustrate the effect the parameter C has on the separation line. A large value of C basically tells our model that we do not have that much faith in our data?s distribution, and will only consider points close to line of separation. A small value of C includes more/all the observations, allowing the margins to be calculated using all the data in the area. print(__doc__) # Code source: Ga Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause

A tutorial exercise for using different SVM kernels. This exercise is used in the Using kernels part of the Supervised learning: predicting an output variable from high-dimensional observations section of the A tutorial on statistical-learning for scientific data processing. print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, svm iris = datasets.load_iris() X = iris.data y = iris.target X = X[y != 0, :2] y = y[y != 0] n_sample = len(X)

Toy example of 1D regression using linear, polynomial and RBF kernels. print(__doc__) import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt Generate sample data X = np.sort(5 * np.random.rand(40, 1), axis=0) y = np.sin(X).ravel() Add noise to targets y[::5] += 3 * (0.5 - np.random.rand(8)) Fit regression model svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1) svr_lin = SVR(kernel='linear', C=1e3) svr_poly = SVR(kernel='poly', C=1e3, degree=2) y_rbf = svr_rbf.fit(X, y).