val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?epsilon:float ->
?tol:float ->
?c:float ->
?loss:string ->
?fit_intercept:bool ->
?intercept_scaling:float ->
?dual:bool ->
?verbose:int ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
?max_iter:int ->
unit ->
tLinear 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 :ref:`User Guide <svm_regression>`.
.. versionadded:: 0.16
Parameters ---------- epsilon : float, optional (default=0.0) 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``.
tol : float, optional (default=1e-4) Tolerance for stopping criteria.
C : float, optional (default=1.0) Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.
loss : string, optional (default='epsilon_insensitive') Specifies the loss function. The epsilon-insensitive loss (standard SVR) is the L1 loss, while the squared epsilon-insensitive loss ('squared_epsilon_insensitive') is the L2 loss.
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.
dual : bool, (default=True) Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features.
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, RandomState instance or None, optional (default=None) The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.
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.
n_iter_ : int Maximum number of iterations run across all classes.
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, tol=1e-5) >>> regr.fit(X, y) LinearSVR(random_state=0, tol=1e-05) >>> print(regr.coef_) 16.35... 26.91... 42.30... 60.47... >>> print(regr.intercept_) -4.29... >>> print(regr.predict([0, 0, 0, 0])) -4.29...
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 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.
val fit :
?sample_weight:Ndarray.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
y:Ndarray.t ->
t ->
tFit the model according to the given training data.
Parameters ---------- X : array-like, sparse matrix of 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 of shape (n_samples,) Target vector relative to X
sample_weight : array-like of shape (n_samples,), default=None Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
Returns ------- self : object
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True 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.
val predict :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict using the linear model.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- C : array, shape (n_samples,) Returns predicted values.
Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The 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 of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float R^2 of self.predict(X) wrt. y.
Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.