NuSVR (original) (raw)
class sklearn.svm.NuSVR(*, nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1)[source]#
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 User Guide.
Parameters:
nufloat, 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.
Cfloat, default=1.0
Penalty parameter C of the error term. For an intuitive visualization of the effects of scaling the regularization parameter C, seeScaling the regularization parameter for SVCs.
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 See Support Vector Regression (SVR) using linear and non-linear kernels
degreeint, 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’.
coef0float, default=0.0
Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
shrinkingbool, default=True
Whether to use the shrinking heuristic. See the User Guide.
tolfloat, default=1e-3
Tolerance for stopping criterion.
cache_sizefloat, default=200
Specify the size of the kernel cache (in MB).
verbosebool, 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_iterint, default=-1
Hard limit on iterations within solver, or -1 for no limit.
Attributes:
coef_ndarray of shape (1, n_features)
Weights assigned to the features when kernel="linear".
**dual_coef_**ndarray of shape (1, n_SV)
Coefficients of the support vector in the decision function.
**fit_status_**int
0 if correctly fitted, 1 otherwise (will raise warning)
**intercept_**ndarray of shape (1,)
Constants in decision function.
**n_features_in_**int
Number of features seen during fit.
Added in version 0.24.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Defined only when Xhas feature names that are all strings.
Added in version 1.0.
**n_iter_**int
Number of iterations run by the optimization routine to fit the model.
Added in version 1.1.
n_support_ndarray of shape (1,), dtype=int32
Number of support vectors for each class.
**shape_fit_**tuple of int of shape (n_dimensions_of_X,)
Array dimensions of training vector X.
**support_**ndarray of shape (n_SV,)
Indices of support vectors.
**support_vectors_**ndarray of shape (n_SV, n_features)
Support vectors.
See also
Support Vector Machine for classification implemented with libsvm with a parameter to control the number of support vectors.
Epsilon Support Vector Machine for regression implemented with libsvm.
References
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))])
property coef_#
Weights assigned to the features when kernel="linear".
Returns:
ndarray of shape (n_features, n_classes)
fit(X, y, sample_weight=None)[source]#
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).
yarray-like of shape (n_samples,)
Target values (class labels in classification, real numbers in regression).
sample_weightarray-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:
selfobject
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()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
get_params(deep=True)[source]#
Get parameters for this estimator.
Parameters:
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
paramsdict
Parameter names mapped to their values.
property n_support_#
Number of support vectors for each class.
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_predndarray of shape (n_samples,)
The predicted values.
score(X, y, sample_weight=None)[source]#
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as\((1 - \frac{u}{v})\), 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:
Xarray-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 with shape(n_samples, n_samples_fitted), where n_samples_fittedis the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
Returns:
scorefloat
\(R^2\) of self.predict(X) w.r.t. y.
Notes
The \(R^2\) score used when calling score on a regressor usesmultioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except forMultiOutputRegressor).
set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → NuSVR[source]#
Request metadata passed to the fit method.
Note that this method is only relevant ifenable_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 tofitif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it tofit.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.
Added 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 aPipeline. Otherwise it has no effect.
Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in fit.
Returns:
selfobject
The updated object.
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:
**paramsdict
Estimator parameters.
Returns:
selfestimator instance
Estimator instance.
set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → NuSVR[source]#
Request metadata passed to the score method.
Note that this method is only relevant ifenable_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 toscoreif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it toscore.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.
Added 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 aPipeline. Otherwise it has no effect.
Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for sample_weight parameter in score.
Returns:
selfobject
The updated object.