sklearn.svm.LinearSVR¶
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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)[源代码]¶ 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 sparse input.
Read more in the User Guide.
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 epsilon-insensitive loss (standard SVR) while ‘l2’ is the squared epsilon-insensitive loss.
epsilon : float, optional (default=0.1)
Epsilon parameter in the epsilon-insensitive 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=1e-4)
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 per-process 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.
参见
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 non-linear 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.
Methods
decision_function(*args, **kwargs)DEPRECATED: and will be removed in 0.19. fit(X, y)Fit the model according to the given training data. get_params([deep])Get parameters for this estimator. predict(X)Predict using the linear model score(X, y[, sample_weight])Returns the coefficient of determination R^2 of the prediction. set_params(**params)Set the parameters of this estimator. -
__init__(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)[源代码]¶
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decision_function(*args, **kwargs)[源代码]¶ DEPRECATED: and will be removed in 0.19.
Decision function of the linear model.
Parameters: X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
Returns: C : array, shape = (n_samples,)
Returns predicted values.
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fit(X, y)[源代码]¶ Fit the model according to the given training data.
Parameters: X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples]
Target vector relative to X
Returns: self : object
Returns self.
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get_params(deep=True)[源代码]¶ Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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predict(X)[源代码]¶ Predict using the linear model
Parameters: X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
Returns: C : array, shape = (n_samples,)
Returns predicted values.
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score(X, y, sample_weight=None)[源代码]¶ Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
R^2 of self.predict(X) wrt. y.