type t =
[ `BaseEstimator
| `BaseLibSVM
| `BaseSVC
| `ClassifierMixin
| `Object
| `SVC ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?c:float ->
?kernel:[ `Linear | `Poly | `Rbf | `Sigmoid | `Precomputed ] ->
?degree:int ->
?gamma:[ `Scale | `Auto | `F of float ] ->
?coef0:float ->
?shrinking:bool ->
?probability:bool ->
?tol:float ->
?cache_size:float ->
?class_weight:[ `Balanced | `DictIntToFloat of (int * float) list ] ->
?verbose:int ->
?max_iter:int ->
?decision_function_shape:[ `Ovo | `Ovr ] ->
?break_ties:bool ->
?random_state:int ->
unit ->
tC-Support Vector Classification.
The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. For large datasets consider using :class:`sklearn.svm.LinearSVC` or :class:`sklearn.linear_model.SGDClassifier` instead, possibly after a :class:`sklearn.kernel_approximation.Nystroem` transformer.
The multiclass support is handled according to a one-vs-one scheme.
For details on the precise mathematical formulation of the provided kernel functions and how `gamma`, `coef0` and `degree` affect each other, see the corresponding section in the narrative documentation: :ref:`svm_kernels`.
Read more in the :ref:`User Guide <svm_classification>`.
Parameters ---------- 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.
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 pre-compute the kernel matrix from data matrices; that matrix should be an array of shape ``(n_samples, n_samples)``.
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'.
shrinking : bool, default=True Whether to use the shrinking heuristic. See the :ref:`User Guide <shrinking_svm>`.
probability : bool, default=False Whether to enable probability estimates. This must be enabled prior to calling `fit`, will slow down that method as it internally uses 5-fold cross-validation, and `predict_proba` may be inconsistent with `predict`. Read more in the :ref:`User Guide <scores_probabilities>`.
tol : float, default=1e-3 Tolerance for stopping criterion.
cache_size : float, default=200 Specify the size of the kernel cache (in MB).
class_weight : dict or 'balanced', default=None Set the parameter C of class i to class_weighti*C for SVC. If not given, all classes are supposed to have weight one. The 'balanced' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``
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.
decision_function_shape : 'ovo', 'ovr', default='ovr' Whether to return a one-vs-rest ('ovr') decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one ('ovo') decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one ('ovo') is always used as multi-class strategy. The parameter is ignored for binary classification.
.. versionchanged:: 0.19 decision_function_shape is 'ovr' by default.
.. versionadded:: 0.17 *decision_function_shape='ovr'* is recommended.
.. versionchanged:: 0.17 Deprecated *decision_function_shape='ovo' and None*.
break_ties : bool, default=False If true, ``decision_function_shape='ovr'``, and number of classes > 2, :term:`predict` will break ties according to the confidence values of :term:`decision_function`; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.
.. versionadded:: 0.22
random_state : int or RandomState instance, default=None Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when `probability` is False. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.
Attributes ---------- support_ : ndarray of shape (n_SV,) Indices of support vectors.
support_vectors_ : ndarray of shape (n_SV, n_features) Support vectors.
n_support_ : ndarray of shape (n_class,), dtype=int32 Number of support vectors for each class.
dual_coef_ : ndarray of shape (n_class-1, n_SV) Dual coefficients of the support vector in the decision function (see :ref:`sgd_mathematical_formulation`), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the :ref:`multi-class section of the User Guide <svm_multi_class>` for details.
coef_ : ndarray of shape (n_class * (n_class-1) / 2, 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 `dual_coef_` and `support_vectors_`.
intercept_ : ndarray of shape (n_class * (n_class-1) / 2,) Constants in decision function.
fit_status_ : int 0 if correctly fitted, 1 otherwise (will raise warning)
classes_ : ndarray of shape (n_classes,) The classes labels.
probA_ : ndarray of shape (n_class * (n_class-1) / 2) probB_ : ndarray of shape (n_class * (n_class-1) / 2) If `probability=True`, it corresponds to the parameters learned in Platt scaling to produce probability estimates from decision values. If `probability=False`, it's an empty array. Platt scaling uses the logistic function ``1 / (1 + exp(decision_value * probA_ + probB_))`` where ``probA_`` and ``probB_`` are learned from the dataset 2_. For more information on the multiclass case and training procedure see section 8 of 1_.
class_weight_ : ndarray of shape (n_class,) Multipliers of parameter C for each class. Computed based on the ``class_weight`` parameter.
shape_fit_ : tuple of int of shape (n_dimensions_of_X,) Array dimensions of training vector ``X``.
Examples -------- >>> import numpy as np >>> from sklearn.pipeline import make_pipeline >>> from sklearn.preprocessing import StandardScaler >>> X = np.array([-1, -1], [-2, -1], [1, 1], [2, 1]) >>> y = np.array(1, 1, 2, 2) >>> from sklearn.svm import SVC >>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto')) >>> clf.fit(X, y) Pipeline(steps=('standardscaler', StandardScaler()),
('svc', SVC(gamma='auto')))
>>> print(clf.predict([-0.8, -1])) 1
See also -------- SVR Support Vector Machine for Regression implemented using libsvm.
LinearSVC Scalable Linear Support Vector Machine for classification implemented using liblinear. Check the See also section of LinearSVC for more comparison element.
References ---------- .. 1 `LIBSVM: A Library for Support Vector Machines <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
.. 2 `Platt, John (1999). 'Probabilistic outputs for support vector machines and comparison to regularizedlikelihood methods.' <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
Evaluates the decision function for the samples in X.
Parameters ---------- X : array-like of shape (n_samples, n_features)
Returns ------- X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2) Returns the decision function of the sample for each class in the model. If decision_function_shape='ovr', the shape is (n_samples, n_classes).
Notes ----- If decision_function_shape='ovo', the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (``coef_``). See also `this question <https://stats.stackexchange.com/questions/14876/ interpreting-distance-from-hyperplane-in-svm>`_ for further details. If decision_function_shape='ovr', the decision function is a monotonic transformation of ovo decision function.
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 classification on samples in X.
For an one-class model, +1 or -1 is returned.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) or (n_samples_test, n_samples_train) For kernel='precomputed', the expected shape of X is (n_samples_test, n_samples_train).
Returns ------- y_pred : ndarray of shape (n_samples,) Class labels for samples in X.
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturn the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
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.
val n_support_ : t -> Py.Object.tAttribute n_support_: get value or raise Not_found if None.
val n_support_opt : t -> Py.Object.t optionAttribute n_support_: get value as an option.
Attribute dual_coef_: get value or raise Not_found if None.
Attribute dual_coef_: get value as an option.
Attribute intercept_: get value or raise Not_found if None.
Attribute intercept_: 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 classes_: get value or raise Not_found if None.
Attribute class_weight_: get value or raise Not_found if None.
Attribute class_weight_: get value as an option.
val shape_fit_ : t -> Py.Object.tAttribute shape_fit_: get value or raise Not_found if None.
val shape_fit_opt : t -> Py.Object.t optionAttribute shape_fit_: 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.