Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `sklearn.svm`.NuSVC

class sklearn.svm.NuSVC(*, nu=0.5, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)

  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 User Guide.

  Parameters:
    nu float, default=0.5

      An upper bound on the fraction of margin errors (see User Guide) and a lower bound of the fraction of support vectors. Should be in the interval (0, 1].

    kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’

      Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix. For an intuitive visualization of different kernel types see Plot classification boundaries with different SVM Kernels.

    degree int, default=3

      Degree of the polynomial kernel function (‘poly’). Must be non-negative. 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

      - if float, must be non-negative.

      Changed in version 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 User Guide.

    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 User Guide.

    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.

      Changed in version 0.19: decision_function_shape is ‘ovr’ by default.

      New in version 0.17: decision_function_shape=’ovr’ is recommended.

      Changed in version 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, predict will break ties according to the confidence values of 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.

      New in version 0.22.

    random_state int, RandomState instance or None, 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 Glossary.

  Attributes:
    class_weight_ndarray of shape (n_classes,)

      Multipliers of parameter C of each class. Computed based on the `class_weight` parameter.

    classes_ndarray of shape (n_classes,)

      The unique classes labels.

    `coef_`ndarray of shape (n_classes * (n_classes -1) / 2, n_features)

      Weights assigned to the features when `kernel="linear"`.

    dual_coef_ndarray of shape (n_classes - 1, n_SV)

      Dual coefficients of the support vector in the decision function (see 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 multi-class section of the User Guide for details.

    fit_status_int

      0 if correctly fitted, 1 if the algorithm did not converge.

    intercept_ndarray of shape (n_classes * (n_classes - 1) / 2,)

      Constants in decision function.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    n_iter_ndarray of shape (n_classes * (n_classes - 1) // 2,)

      Number of iterations run by the optimization routine to fit the model. The shape of this attribute depends on the number of models optimized which in turn depends on the number of classes.

      New in version 1.1.

    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_classes,), dtype=int32

      Number of support vectors for each class.

    fit_status_int

      0 if correctly fitted, 1 if the algorithm did not converge.

    `probA_`ndarray of shape (n_classes * (n_classes - 1) / 2,)

      Parameter learned in Platt scaling when `probability=True`.

    `probB_`ndarray of shape (n_classes * (n_classes - 1) / 2,)

      Parameter learned in Platt scaling when `probability=True`.

    shape_fit_tuple of int of shape (n_dimensions_of_X,)

      Array dimensions of training vector `X`.

  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

  [2]

  Platt, John (1999). “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods”

  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]

  Methods

  `decision_function`(X) | Evaluate the decision function for the samples in X.
  `fit`(X, y[, sample_weight]) | Fit the SVM model according to the given training data.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Perform classification on samples in X.
  `predict_log_proba`(X) | Compute log probabilities of possible outcomes for samples in X.
  `predict_proba`(X) | Compute probabilities of possible outcomes for samples in X.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.

  property coef_

    Weights assigned to the features when `kernel="linear"`.

    Returns:
      ndarray of shape (n_features, n_classes)
  decision_function(X)

    Evaluate the decision function for the samples in X.

    Parameters:
      X array-like of shape (n_samples, n_features)

        The input samples.

    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 for further details. If decision_function_shape=’ovr’, the decision function is a monotonic transformation of ovo decision function.

  fit(X, y, sample_weight=None)

    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

        Fitted estimator.

    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_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    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 dict

        Parameter names mapped to their values.

  property n_support_

    Number of support vectors for each class.

  predict(X)

    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.

  predict_log_proba(X)

    Compute log probabilities of possible outcomes for samples in X.

    The model need to have probability information computed at training time: fit with attribute `probability` set to True.

    Parameters:
      X array-like 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:
      T ndarray of shape (n_samples, n_classes)

        Returns the log-probabilities of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

    Notes

    The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

  predict_proba(X)

    Compute probabilities of possible outcomes for samples in X.

    The model needs to have probability information computed at training time: fit with attribute `probability` set to True.

    Parameters:
      X array-like of shape (n_samples, n_features)

        For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

    Returns:
      T ndarray of shape (n_samples, n_classes)

        Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

    Notes

    The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

  property probA_

    Parameter learned in Platt scaling when `probability=True`.

    Returns:
      ndarray of shape (n_classes * (n_classes - 1) / 2)
  property probB_

    Parameter learned in Platt scaling when `probability=True`.

    Returns:
      ndarray of shape (n_classes * (n_classes - 1) / 2)
  score(X, y, sample_weight=None)

    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)` w.r.t. `y`.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → NuSVC

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). 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 estimator instance

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → NuSVC

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.svm.NuSVC`

Non-linear SVM

Non-linear SVM