Svm

LinearSVC

Module Sklearn.​Svm.​LinearSVC wraps Python class sklearn.svm.LinearSVC.

type t

create

constructor and attributes create

val create :
  ?penalty:[`L1 | `L2] ->
  ?loss:[`Hinge | `Squared_hinge] ->
  ?dual:bool ->
  ?tol:float ->
  ?c:float ->
  ?multi_class:[`Ovr | `Crammer_singer] ->
  ?fit_intercept:bool ->
  ?intercept_scaling:float ->
  ?class_weight:[`Balanced | `DictIntToFloat of (int * float) list] ->
  ?verbose:int ->
  ?random_state:int ->
  ?max_iter:int ->
  unit ->
  t

Linear Support Vector Classification.

Similar to SVC 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 and the multiclass support is handled according to a one-vs-the-rest scheme.

Read more in the :ref:User Guide <svm_classification>.

Parameters

• penalty : {'l1', 'l2'}, default='l2' Specifies the norm used in the penalization. The 'l2' penalty is the standard used in SVC. The 'l1' leads to coef_ vectors that are sparse.

• loss : {'hinge', 'squared_hinge'}, default='squared_hinge' Specifies the loss function. 'hinge' is the standard SVM loss (used e.g. by the SVC class) while 'squared_hinge' is the square of the hinge loss.

• 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, default=1e-4 Tolerance for stopping criteria.

• C : float, default=1.0 Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.

• multi_class : {'ovr', 'crammer_singer'}, default='ovr' Determines the multi-class strategy if y contains more than two classes. 'ovr' trains n_classes one-vs-rest classifiers, while 'crammer_singer' optimizes a joint objective over all classes. While crammer_singer is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If 'crammer_singer' is chosen, the options loss, penalty and dual will be ignored.

• fit_intercept : bool, 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, 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.

• class_weight : dict or 'balanced', default=None Set the parameter C of class i to class_weight[i]*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 : 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 or RandomState instance, default=None Controls the pseudo random number generation for shuffling the data for the dual coordinate descent (if dual=True). When dual=False the underlying implementation of :class:LinearSVC is not random and random_state has no effect on the results. Pass an int for reproducible output across multiple function calls.

• See :term:Glossary <random_state>.

• max_iter : int, default=1000 The maximum number of iterations to be run.

Attributes

• coef_ : ndarray of shape (1, 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_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,) Constants in decision function.

• classes_ : ndarray of shape (n_classes,) The unique classes labels.

• n_iter_ : int Maximum number of iterations run across all classes.

See Also

SVC Implementation of Support Vector Machine classifier using libsvm: the kernel can be non-linear but its SMO algorithm does not scale to large number of samples as LinearSVC does.

Furthermore SVC multi-class mode is implemented using one
vs one scheme while LinearSVC uses one vs the rest. It is
possible to implement one vs the rest with SVC by using the
:class:`sklearn.multiclass.OneVsRestClassifier` wrapper.

Finally SVC can fit dense data without memory copy if the input
is C-contiguous. Sparse data will still incur memory copy though.

sklearn.linear_model.SGDClassifier SGDClassifier can optimize the same cost function as LinearSVC 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.

Notes

The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.

The underlying implementation, liblinear, uses a sparse internal representation for the data that will incur a memory copy.

Predict output may not match that of standalone liblinear in certain cases. See :ref:differences from liblinear <liblinear_differences> in the narrative documentation.

References

LIBLINEAR: A Library for Large Linear Classification <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>__

Examples

>>> from sklearn.svm import LinearSVC
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = make_pipeline(StandardScaler(),
...                     LinearSVC(random_state=0, tol=1e-5))
>>> clf.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('linearsvc', LinearSVC(random_state=0, tol=1e-05))])

>>> print(clf.named_steps['linearsvc'].coef_)
[[0.141...   0.526... 0.679... 0.493...]]

>>> print(clf.named_steps['linearsvc'].intercept_)
[0.1693...]
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]

decision_function

method decision_function

val decision_function :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters

• X : array_like or sparse matrix, shape (n_samples, n_features) Samples.

Returns

array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

densify

method densify

val densify :
  [> tag] Obj.t ->
  t

Convert coefficient matrix to dense array format.

Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

Returns

self Fitted estimator.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit 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.

  .. versionadded:: 0.18

Returns

• self : object An instance of the estimator.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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.

predict

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict class labels for samples in X.

Parameters

• X : array_like or sparse matrix, shape (n_samples, n_features) Samples.

Returns

• C : array, shape [n_samples] Predicted class label per sample.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return 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.

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set 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.

sparsify

method sparsify

val sparsify :
  [> tag] Obj.t ->
  t

Convert coefficient matrix to sparse format.

Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The intercept_ member is not converted.

Returns

self Fitted estimator.

Notes

For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.

After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.

coef_

attribute coef_

val coef_ : t -> [>`ArrayLike] Np.Obj.t
val coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

intercept_

attribute intercept_

val intercept_ : t -> [>`ArrayLike] Np.Obj.t
val intercept_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

classes_

attribute classes_

val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_iter_

attribute n_iter_

val n_iter_ : t -> int
val n_iter_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

LinearSVR

Module Sklearn.​Svm.​LinearSVR wraps Python class sklearn.svm.LinearSVR.

type t

create

constructor and attributes create

val create :
  ?epsilon:float ->
  ?tol:float ->
  ?c:float ->
  ?loss:[`Epsilon_insensitive | `Squared_epsilon_insensitive] ->
  ?fit_intercept:bool ->
  ?intercept_scaling:float ->
  ?dual:bool ->
  ?verbose:int ->
  ?random_state:int ->
  ?max_iter:int ->
  unit ->
  t

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 :ref:User Guide <svm_regression>.

.. versionadded:: 0.16

Parameters

• epsilon : float, 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, default=1e-4 Tolerance for stopping criteria.

• C : float, default=1.0 Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.

• loss : {'epsilon_insensitive', 'squared_epsilon_insensitive'}, 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 : bool, 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, 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 or RandomState instance, default=None Controls the pseudo random number generation for shuffling the data. Pass an int for reproducible output across multiple function calls.

• See :term:Glossary <random_state>.

• max_iter : int, default=1000 The maximum number of iterations to be run.

Attributes

• coef_ : ndarray of 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_ : ndarray of 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.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = make_pipeline(StandardScaler(),
...                      LinearSVR(random_state=0, tol=1e-5))
>>> regr.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('linearsvr', LinearSVR(random_state=0, tol=1e-05))])

>>> print(regr.named_steps['linearsvr'].coef_)
[18.582... 27.023... 44.357... 64.522...]
>>> print(regr.named_steps['linearsvr'].intercept_)
[-4...]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-2.384...]

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.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit 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.

  .. versionadded:: 0.18

Returns

• self : object An instance of the estimator.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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.

predict

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Predict 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.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

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 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).

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set 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.

coef_

attribute coef_

val coef_ : t -> [>`ArrayLike] Np.Obj.t
val coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

intercept_

attribute intercept_

val intercept_ : t -> [>`ArrayLike] Np.Obj.t
val intercept_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_iter_

attribute n_iter_

val n_iter_ : t -> int
val n_iter_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

NuSVC

Module Sklearn.​Svm.​NuSVC wraps Python class sklearn.svm.NuSVC.

type t

create

constructor and attributes create

val create :
  ?nu:float ->
  ?kernel:[`Linear | `Poly | `Rbf | `Sigmoid | `Precomputed] ->
  ?degree:int ->
  ?gamma:[`F of float | `Scale | `Auto] ->
  ?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 ->
  t

Nu-Support Vector Classification.

Similar to SVC but uses a parameter to control the number of support vectors.

The implementation is based on libsvm.

Read more in the :ref:User Guide <svm_classification>.

Parameters

• nu : float, default=0.5 An upper bound on the fraction of margin errors (see :ref:User Guide <nu_svc>) and a lower bound of the fraction of support vectors. Should be in the interval (0, 1].

• 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'.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,
  • if 'auto', uses 1 / n_features.

  .. 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, 'balanced'}, default=None Set the parameter C of class i to class_weight[i]*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 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 readonly property derived from dual_coef_ and support_vectors_.

• intercept_ : ndarray of shape (n_class * (n_class-1) / 2,) Constants in decision function.

• classes_ : ndarray of shape (n_classes,) The unique classes labels.

• fit_status_ : int 0 if correctly fitted, 1 if the algorithm did not converge.

• 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 of 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
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.svm import NuSVC
>>> clf = make_pipeline(StandardScaler(), NuSVC())
>>> clf.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()), ('nusvc', NuSVC())])
>>> print(clf.predict([[-0.8, -1]]))
[1]

See also

SVC Support Vector Machine for classification using libsvm.

LinearSVC Scalable linear Support Vector Machine for classification using liblinear.

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>_

decision_function

method decision_function

val decision_function :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit 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_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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.

predict

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return 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.

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set 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.

support_

attribute support_

val support_ : t -> [>`ArrayLike] Np.Obj.t
val support_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

support_vectors_

attribute support_vectors_

val support_vectors_ : t -> [>`ArrayLike] Np.Obj.t
val support_vectors_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_support_

attribute n_support_

val n_support_ : t -> Py.Object.t
val n_support_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

dual_coef_

attribute dual_coef_

val dual_coef_ : t -> [>`ArrayLike] Np.Obj.t
val dual_coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

coef_

attribute coef_

val coef_ : t -> [>`ArrayLike] Np.Obj.t
val coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

intercept_

attribute intercept_

val intercept_ : t -> [>`ArrayLike] Np.Obj.t
val intercept_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

classes_

attribute classes_

val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

fit_status_

attribute fit_status_

val fit_status_ : t -> int
val fit_status_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

probA_

attribute probA_

val probA_ : t -> [>`ArrayLike] Np.Obj.t
val probA_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

class_weight_

attribute class_weight_

val class_weight_ : t -> [>`ArrayLike] Np.Obj.t
val class_weight_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

shape_fit_

attribute shape_fit_

val shape_fit_ : t -> Py.Object.t
val shape_fit_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

NuSVR

Module Sklearn.​Svm.​NuSVR wraps Python class sklearn.svm.NuSVR.

type t

create

constructor and attributes create

val create :
  ?nu:float ->
  ?c:float ->
  ?kernel:[`Linear | `Poly | `Rbf | `Sigmoid | `Precomputed] ->
  ?degree:int ->
  ?gamma:[`F of float | `Scale | `Auto] ->
  ?coef0:float ->
  ?shrinking:bool ->
  ?tol:float ->
  ?cache_size:float ->
  ?verbose:int ->
  ?max_iter:int ->
  unit ->
  t

Nu Support Vector Regression.

Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-SVR.

The implementation is based on libsvm.

Read more in the :ref:User Guide <svm_regression>.

Parameters

• nu : float, default=0.5 An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.

• C : float, default=1.0 Penalty parameter C of the error term.

• 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'.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,
  • if 'auto', uses 1 / n_features.

  .. 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>.

• tol : float, default=1e-3 Tolerance for stopping criterion.

• 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_.

• intercept_ : ndarray of shape (1,) Constants in decision function.

Examples

>>> from sklearn.svm import NuSVR
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> regr = make_pipeline(StandardScaler(), NuSVR(C=1.0, nu=0.1))
>>> regr.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('nusvr', NuSVR(nu=0.1))])

See also

NuSVC Support Vector Machine for classification implemented with libsvm with a parameter to control the number of support vectors.

SVR epsilon Support Vector Machine for regression implemented with libsvm.

Notes

• *References:* LIBSVM: A Library for Support Vector Machines <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>__

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit 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_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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.

predict

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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,)

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

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 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).

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set 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.

support_

attribute support_

val support_ : t -> [>`ArrayLike] Np.Obj.t
val support_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

support_vectors_

attribute support_vectors_

val support_vectors_ : t -> [>`ArrayLike] Np.Obj.t
val support_vectors_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

dual_coef_

attribute dual_coef_

val dual_coef_ : t -> [>`ArrayLike] Np.Obj.t
val dual_coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

coef_

attribute coef_

val coef_ : t -> [>`ArrayLike] Np.Obj.t
val coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

intercept_

attribute intercept_

val intercept_ : t -> [>`ArrayLike] Np.Obj.t
val intercept_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

OneClassSVM

Module Sklearn.​Svm.​OneClassSVM wraps Python class sklearn.svm.OneClassSVM.

type t

create

constructor and attributes create

val create :
  ?kernel:[`Linear | `Poly | `Rbf | `Sigmoid | `Precomputed] ->
  ?degree:int ->
  ?gamma:[`F of float | `Scale | `Auto] ->
  ?coef0:float ->
  ?tol:float ->
  ?nu:float ->
  ?shrinking:bool ->
  ?cache_size:float ->
  ?verbose:int ->
  ?max_iter:int ->
  unit ->
  t

Unsupervised Outlier Detection.

Estimate the support of a high-dimensional distribution.

The implementation is based on libsvm.

Read more in the :ref:User Guide <outlier_detection>.

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'.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,
  • if 'auto', uses 1 / n_features.

  .. 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.

• nu : float, default=0.5 An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.

• 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 vectors 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_

• intercept_ : ndarray of shape (1,) Constant in the decision function.

• offset_ : float Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset_. The offset is the opposite of intercept_ and is provided for consistency with other outlier detection algorithms.

  .. versionadded:: 0.20

• fit_status_ : int 0 if correctly fitted, 1 otherwise (will raise warning)

Examples

>>> from sklearn.svm import OneClassSVM
>>> X = [[0], [0.44], [0.45], [0.46], [1]]
>>> clf = OneClassSVM(gamma='auto').fit(X)
>>> clf.predict(X)
array([-1,  1,  1,  1, -1])
>>> clf.score_samples(X)
array([1.7798..., 2.0547..., 2.0556..., 2.0561..., 1.7332...])

decision_function

method decision_function

val decision_function :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Signed distance to the separating hyperplane.

Signed distance is positive for an inlier and negative for an outlier.

Parameters

• X : array-like of shape (n_samples, n_features) The data matrix.

Returns

• dec : ndarray of shape (n_samples,) Returns the decision function of the samples.

fit

method fit

val fit :
  ?y:Py.Object.t ->
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  ?params:(string * Py.Object.t) list ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Detects the soft boundary of the set of samples X.

Parameters

• X : {array-like, sparse matrix} of shape (n_samples, n_features) Set of samples, where n_samples is the number of samples and n_features is the number of features.

• 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.

• y : Ignored not used, present for API consistency by convention.

Returns

• self : object

Notes

If X is not a C-ordered contiguous array it is copied.

fit_predict

method fit_predict

val fit_predict :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters

• X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features)

• y : Ignored Not used, present for API consistency by convention.

Returns

• y : ndarray of shape (n_samples,) 1 for inliers, -1 for outliers.

get_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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.

predict

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Perform classification on samples in X.

For a 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.

score_samples

method score_samples

val score_samples :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Raw scoring function of the samples.

Parameters

• X : array-like of shape (n_samples, n_features) The data matrix.

Returns

• score_samples : ndarray of shape (n_samples,) Returns the (unshifted) scoring function of the samples.

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set 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.

support_

attribute support_

val support_ : t -> [>`ArrayLike] Np.Obj.t
val support_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

support_vectors_

attribute support_vectors_

val support_vectors_ : t -> [>`ArrayLike] Np.Obj.t
val support_vectors_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

dual_coef_

attribute dual_coef_

val dual_coef_ : t -> [>`ArrayLike] Np.Obj.t
val dual_coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

coef_

attribute coef_

val coef_ : t -> [>`ArrayLike] Np.Obj.t
val coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

intercept_

attribute intercept_

val intercept_ : t -> [>`ArrayLike] Np.Obj.t
val intercept_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

offset_

attribute offset_

val offset_ : t -> float
val offset_opt : t -> (float) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

fit_status_

attribute fit_status_

val fit_status_ : t -> int
val fit_status_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

SVC

Module Sklearn.​Svm.​SVC wraps Python class sklearn.svm.SVC.

type t

create

constructor and attributes create

val create :
  ?c:float ->
  ?kernel:[`Linear | `Poly | `Rbf | `Sigmoid | `Precomputed] ->
  ?degree:int ->
  ?gamma:[`F of float | `Scale | `Auto] ->
  ?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 ->
  t

C-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'.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,
  • if 'auto', uses 1 / n_features.

  .. 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_weight[i]*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>_

decision_function

method decision_function

val decision_function :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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.

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit 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_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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.

predict

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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.

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return 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.

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set 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.

support_

attribute support_

val support_ : t -> [>`ArrayLike] Np.Obj.t
val support_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

support_vectors_

attribute support_vectors_

val support_vectors_ : t -> [>`ArrayLike] Np.Obj.t
val support_vectors_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

n_support_

attribute n_support_

val n_support_ : t -> Py.Object.t
val n_support_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

dual_coef_

attribute dual_coef_

val dual_coef_ : t -> [>`ArrayLike] Np.Obj.t
val dual_coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

coef_

attribute coef_

val coef_ : t -> [>`ArrayLike] Np.Obj.t
val coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

intercept_

attribute intercept_

val intercept_ : t -> [>`ArrayLike] Np.Obj.t
val intercept_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

fit_status_

attribute fit_status_

val fit_status_ : t -> int
val fit_status_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

classes_

attribute classes_

val classes_ : t -> [>`ArrayLike] Np.Obj.t
val classes_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

probA_

attribute probA_

val probA_ : t -> [>`ArrayLike] Np.Obj.t
val probA_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

class_weight_

attribute class_weight_

val class_weight_ : t -> [>`ArrayLike] Np.Obj.t
val class_weight_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

shape_fit_

attribute shape_fit_

val shape_fit_ : t -> Py.Object.t
val shape_fit_opt : t -> (Py.Object.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

SVR

Module Sklearn.​Svm.​SVR wraps Python class sklearn.svm.SVR.

type t

create

constructor and attributes create

val create :
  ?kernel:[`Linear | `Poly | `Rbf | `Sigmoid | `Precomputed] ->
  ?degree:int ->
  ?gamma:[`F of float | `Scale | `Auto] ->
  ?coef0:float ->
  ?tol:float ->
  ?c:float ->
  ?epsilon:float ->
  ?shrinking:bool ->
  ?cache_size:float ->
  ?verbose:int ->
  ?max_iter:int ->
  unit ->
  t

Epsilon-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'.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,
  • if 'auto', uses 1 / n_features.

  .. 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>__

fit

method fit

val fit :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  t

Fit 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_params

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

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.

predict

method predict

val predict :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

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,)

score

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

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 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).

set_params

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set 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.

support_

attribute support_

val support_ : t -> [>`ArrayLike] Np.Obj.t
val support_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

support_vectors_

attribute support_vectors_

val support_vectors_ : t -> [>`ArrayLike] Np.Obj.t
val support_vectors_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

dual_coef_

attribute dual_coef_

val dual_coef_ : t -> [>`ArrayLike] Np.Obj.t
val dual_coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

coef_

attribute coef_

val coef_ : t -> [>`ArrayLike] Np.Obj.t
val coef_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

fit_status_

attribute fit_status_

val fit_status_ : t -> int
val fit_status_opt : t -> (int) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

intercept_

attribute intercept_

val intercept_ : t -> [>`ArrayLike] Np.Obj.t
val intercept_opt : t -> ([>`ArrayLike] Np.Obj.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

to_string

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show

method show

val show: t -> string

Print the object to a human-readable representation.

pp

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

l1_min_c

function l1_min_c

val l1_min_c :
  ?loss:[`Squared_hinge | `Log] ->
  ?fit_intercept:bool ->
  ?intercept_scaling:float ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  unit ->
  float

Return the lowest bound for C such that for C in (l1_min_C, infinity) the model is guaranteed not to be empty. This applies to l1 penalized classifiers, such as LinearSVC with penalty='l1' and linear_model.LogisticRegression with penalty='l1'.

This value is valid if class_weight parameter in fit() is not set.

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.

• loss : {'squared_hinge', 'log'}, default='squared_hinge' Specifies the loss function. With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss). With 'log' it is the loss of logistic regression models.

• fit_intercept : bool, default=True Specifies if the intercept should be fitted by the model. It must match the fit() method parameter.

• intercept_scaling : float, default=1.0 when fit_intercept is True, instance vector x becomes [x, intercept_scaling], i.e. a 'synthetic' feature with constant value equals to intercept_scaling is appended to the instance vector. It must match the fit() method parameter.

Returns

• l1_min_c : float minimum value for C