sklearn.svm.NuSVR
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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
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nufloat, default=0.5
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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.
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Cfloat, default=1.0
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Penalty parameter C of the error term.
-
kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, default=’rbf’
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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.
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degreeint, default=3
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Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
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gamma{‘scale’, ‘auto’} or float, default=’scale’
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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’. - if
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coef0float, default=0.0
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Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
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shrinkingbool, default=True
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Whether to use the shrinking heuristic. See the User Guide.
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tolfloat, default=1e-3
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Tolerance for stopping criterion.
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cache_sizefloat, default=200
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Specify the size of the kernel cache (in MB).
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verbosebool, default=False
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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.
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max_iterint, default=-1
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Hard limit on iterations within solver, or -1 for no limit.
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- Attributes
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class_weight_ndarray of shape (n_classes,)
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Multipliers of parameter C for each class. Computed based on the
class_weight
parameter. -
coef_ndarray of shape (1, n_features)
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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 fromdual_coef_
andsupport_vectors_
. -
dual_coef_ndarray of shape (1, n_SV)
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Coefficients of the support vector in the decision function.
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fit_status_int
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0 if correctly fitted, 1 otherwise (will raise warning)
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intercept_ndarray of shape (1,)
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Constants in decision function.
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n_support_ndarray of shape (n_classes,), dtype=int32
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Number of support vectors for each class.
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shape_fit_tuple of int of shape (n_dimensions_of_X,)
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Array dimensions of training vector
X
. -
support_ndarray of shape (n_SV,)
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Indices of support vectors.
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support_vectors_ndarray of shape (n_SV, n_features)
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Support vectors.
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See also
References
-
1
-
2
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 \(R^2\) of the prediction.
set_params
(**params)Set the parameters of this estimator.
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fit(X, y, sample_weight=None)
[source] -
Fit the SVM model according to the given training data.
- Parameters
-
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X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)
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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,)
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Target values (class labels in classification, real numbers in regression).
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sample_weightarray-like of shape (n_samples,), default=None
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Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
-
- Returns
-
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selfobject
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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.
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get_params(deep=True)
[source] -
Get parameters for this estimator.
- Parameters
-
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deepbool, default=True
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If True, will return the parameters for this estimator and contained subobjects that are estimators.
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- Returns
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paramsdict
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Parameter names mapped to their values.
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predict(X)
[source] -
Perform regression on samples in X.
For an one-class model, +1 (inlier) or -1 (outlier) is returned.
- Parameters
-
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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
-
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y_predndarray of shape (n_samples,)
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score(X, y, sample_weight=None)
[source] -
Return the coefficient of determination \(R^2\) of the prediction.
The coefficient \(R^2\) 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)
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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
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Sample weights.
-
- Returns
-
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scorefloat
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\(R^2\) of
self.predict(X)
wrt.y
.
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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
-
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**paramsdict
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Estimator parameters.
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- Returns
-
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selfestimator instance
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Estimator instance.
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Examples using sklearn.svm.NuSVR
© 2007–2020 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.svm.NuSVR.html