sklearn.svm
.NuSVR¶
- 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.
- 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.
- degreeint, 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.
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:
- class_weight_ndarray of shape (n_classes,)
Multipliers of parameter C for each class. Computed based on the
class_weight
parameter.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.
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_int
Number of iterations run by the optimization routine to fit the model.
New in version 1.1.
n_support_
ndarray of shape (1,), dtype=int32Number 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
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))])
Methods
fit
(X, y[, sample_weight])Fit the SVM model according to the given training data.
get_params
([deep])Get parameters for this estimator.
predict
(X)Perform regression on samples in X.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
- 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 andn_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_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.
- predict(X)[source]¶
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 ofy
, 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)
, wheren_samples_fitted
is 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)
wrt.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 ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_params(**params)[source]¶
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.