type t =
[ `BaseEstimator | `BaseLibSVM | `Object | `RegressorMixin | `SVR ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?kernel:[ `Linear | `Poly | `Rbf | `Sigmoid | `Precomputed ] ->
?degree:int ->
?gamma:[ `Scale | `Auto | `F of float ] ->
?coef0:float ->
?tol:float ->
?c:float ->
?epsilon:float ->
?shrinking:bool ->
?cache_size:float ->
?verbose:int ->
?max_iter:int ->
unit ->
tEpsilon-Support Vector Regression.
The free parameters in the model are C and epsilon.
The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to datasets with more than a couple of 10000 samples. For large datasets consider using :class:`sklearn.svm.LinearSVR` or :class:`sklearn.linear_model.SGDRegressor` instead, possibly after a :class:`sklearn.kernel_approximation.Nystroem` transformer.
Read more in the :ref:`User Guide <svm_regression>`.
Parameters ---------- kernel : 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed', default='rbf' Specifies the kernel type to be used in the algorithm. It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or a callable. If none is given, 'rbf' will be used. If a callable is given it is used to precompute the kernel matrix.
degree : int, default=3 Degree of the polynomial kernel function ('poly'). Ignored by all other kernels.
gamma : 'scale', 'auto' or float, default='scale' Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
.. versionchanged:: 0.22 The default value of ``gamma`` changed from 'auto' to 'scale'.
coef0 : float, default=0.0 Independent term in kernel function. It is only significant in 'poly' and 'sigmoid'.
tol : float, default=1e-3 Tolerance for stopping criterion.
C : float, default=1.0 Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.
epsilon : float, default=0.1 Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value.
shrinking : bool, default=True Whether to use the shrinking heuristic. See the :ref:`User Guide <shrinking_svm>`.
cache_size : float, default=200 Specify the size of the kernel cache (in MB).
verbose : bool, default=False Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
max_iter : int, default=-1 Hard limit on iterations within solver, or -1 for no limit.
Attributes ---------- support_ : ndarray of shape (n_SV,) Indices of support vectors.
support_vectors_ : ndarray of shape (n_SV, n_features) Support vectors.
dual_coef_ : ndarray of shape (1, n_SV) Coefficients of the support vector in the decision function.
coef_ : ndarray of shape (1, 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 readonly property derived from `dual_coef_` and `support_vectors_`.
fit_status_ : int 0 if correctly fitted, 1 otherwise (will raise warning)
intercept_ : ndarray of shape (1,) Constants in decision function.
Examples -------- >>> from sklearn.svm import SVR >>> from sklearn.pipeline import make_pipeline >>> from sklearn.preprocessing import StandardScaler >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2)) >>> regr.fit(X, y) Pipeline(steps=('standardscaler', StandardScaler()),
('svr', SVR(epsilon=0.2)))
See also -------- NuSVR Support Vector Machine for regression implemented using libsvm using a parameter to control the number of support vectors.
LinearSVR Scalable Linear Support Vector Machine for regression implemented using liblinear.
Notes ----- **References:** `LIBSVM: A Library for Support Vector Machines <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`__
val fit :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tFit the SVM model according to the given training data.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) or (n_samples, n_samples) Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel='precomputed', the expected shape of X is (n_samples, n_samples).
y : array-like of shape (n_samples,) Target values (class labels in classification, real numbers in regression)
sample_weight : array-like of shape (n_samples,), default=None Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
Returns ------- self : object
Notes ----- If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.
If X is a dense array, then the other methods will not support sparse matrices as input.
Get 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.
Perform regression on samples in X.
For an one-class model, +1 (inlier) or -1 (outlier) is returned.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) For kernel='precomputed', the expected shape of X is (n_samples_test, n_samples_train).
Returns ------- y_pred : ndarray of shape (n_samples,)
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturn 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 uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.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.
Attribute support_: get value or raise Not_found if None.
Attribute support_vectors_: get value or raise Not_found if None.
Attribute support_vectors_: get value as an option.
Attribute dual_coef_: get value or raise Not_found if None.
Attribute dual_coef_: get value as an option.
val fit_status_ : t -> intAttribute fit_status_: get value or raise Not_found if None.
val fit_status_opt : t -> int optionAttribute fit_status_: get value as an option.
Attribute intercept_: get value or raise Not_found if None.
Attribute intercept_: get value as an option.
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 : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.