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Linear Support Vector Regression. This node has been automatically generated by wrapping the ``sklearn.svm.classes.LinearSVR`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. 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 sparse input. Read more in the :ref:`User Guide <svm_regression>`. **Parameters** C : float, optional (default=1.0) Penalty parameter C of the error term. The penalty is a squared l2 penalty. The bigger this parameter, the less regularization is used. loss : string, 'epsilon_insensitive' or 'squared_epsilon_insensitive' (default='epsilon_insensitive') Specifies the loss function. 'l1' is the epsiloninsensitive loss (standard SVR) while 'l2' is the squared epsiloninsensitive loss. epsilon : float, optional (default=0.1) Epsilon parameter in the epsiloninsensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set ``epsilon=0``. dual : bool, (default=True) Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. tol : float, optional (default=1e4) Tolerance for stopping criteria. fit_intercept : boolean, optional (default=True) Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered). intercept_scaling : float, optional (default=1) When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. verbose : int, (default=0) Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context. random_state : int seed, RandomState instance, or None (default=None) The seed of the pseudo random number generator to use when shuffling the data. max_iter : int, (default=1000) The maximum number of iterations to be run. **Attributes** ``coef_`` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. `coef_` is a readonly property derived from `raw_coef_` that follows the internal memory layout of liblinear. ``intercept_`` : array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function. See also LinearSVC Implementation of Support Vector Machine classifier using the same library as this class (liblinear). SVR Implementation of Support Vector Machine regression using libsvm:  the kernel can be nonlinear but its SMO algorithm does not  scale to large number of samples as LinearSVC does. sklearn.linear_model.SGDRegressor SGDRegressor can optimize the same cost function as LinearSVR by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.














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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Linear Support Vector Regression. This node has been automatically generated by wrapping the ``sklearn.svm.classes.LinearSVR`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. 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 sparse input. Read more in the :ref:`User Guide <svm_regression>`. **Parameters** C : float, optional (default=1.0) Penalty parameter C of the error term. The penalty is a squared l2 penalty. The bigger this parameter, the less regularization is used. loss : string, 'epsilon_insensitive' or 'squared_epsilon_insensitive' (default='epsilon_insensitive') Specifies the loss function. 'l1' is the epsiloninsensitive loss (standard SVR) while 'l2' is the squared epsiloninsensitive loss. epsilon : float, optional (default=0.1) Epsilon parameter in the epsiloninsensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set ``epsilon=0``. dual : bool, (default=True) Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. tol : float, optional (default=1e4) Tolerance for stopping criteria. fit_intercept : boolean, optional (default=True) Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered). intercept_scaling : float, optional (default=1) When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased. verbose : int, (default=0) Enable verbose output. Note that this setting takes advantage of a perprocess runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context. random_state : int seed, RandomState instance, or None (default=None) The seed of the pseudo random number generator to use when shuffling the data. max_iter : int, (default=1000) The maximum number of iterations to be run. **Attributes** ``coef_`` : array, shape = [n_features] if n_classes == 2 else [n_classes, n_features] Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel. `coef_` is a readonly property derived from `raw_coef_` that follows the internal memory layout of liblinear. ``intercept_`` : array, shape = [1] if n_classes == 2 else [n_classes] Constants in decision function. See also LinearSVC Implementation of Support Vector Machine classifier using the same library as this class (liblinear). SVR Implementation of Support Vector Machine regression using libsvm:  the kernel can be nonlinear but its SMO algorithm does not  scale to large number of samples as LinearSVC does. sklearn.linear_model.SGDRegressor SGDRegressor can optimize the same cost function as LinearSVR by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.




Predict using the linear model This node has been automatically generated by wrapping the sklearn.svm.classes.LinearSVR class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns



Fit the model according to the given training data. This node has been automatically generated by wrapping the sklearn.svm.classes.LinearSVR class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

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