8.24.2.4. sklearn.svm.sparse.NuSVR

class sklearn.svm.sparse.NuSVR(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, epsilon=0.1, probability=False, tol=0.001, cache_size=200, scale_C=False)

NuSVR for sparse matrices (csr)

See sklearn.svm.NuSVC for a complete list of parameters

Notes

For best results, this accepts a matrix in csr format (scipy.sparse.csr), but should be able to convert from any array-like object (including other sparse representations).

Examples

>>> from sklearn.svm.sparse import NuSVR
>>> 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)
>>> clf = NuSVR(nu=0.1, C=1.0)
>>> clf.fit(X, y)
NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.2,
   kernel='rbf', nu=0.1, probability=False, scale_C=False, shrinking=True,
   tol=0.001)

Methods

fit(X, y[, class_weight, sample_weight])	Fit the SVM model according to the given training data and parameters.
predict(T)	This function does classification or regression on an array of
predict_log_proba(T)	Compute the log likehoods each possible outcomes of samples in T.
predict_proba(X)	This function does classification or regression on a test vector X
score(X, y)	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of the estimator.

__init__(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, epsilon=0.1, probability=False, tol=0.001, cache_size=200, scale_C=False)

fit(X, y, class_weight=None, sample_weight=None)

Fit the SVM model according to the given training data and parameters.

Parameters :

X : sparse matrix, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples]

Target values (integers in classification, real numbers in regression)

class_weight : {dict, ‘auto’}, optional

Weights associated with classes in the form {class_label : weight}. If not given, all classes are supposed to have weight one.

The ‘auto’ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies.

sample_weight : array-like, shape = [n_samples], optional

Weights applied to individual samples (1. for unweighted).

Returns :

self : object

Returns an instance of self.

Notes

For maximum effiency, use a sparse matrix in csr format (scipy.sparse.csr_matrix)

predict(T)

This function does classification or regression on an array of test vectors T.

For a classification model, the predicted class for each sample in T is returned. For a regression model, the function value of T calculated is returned.

For an one-class model, +1 or -1 is returned.

Parameters :T : scipy.sparse.csr, shape = [n_samples, n_features] Returns :C : array, shape = [n_samples]

predict_log_proba(T)

Compute the log likehoods each possible outcomes of samples in T.

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

Parameters :

T : array-like, shape = [n_samples, n_features]

Returns :

T : array-like, shape = [n_samples, n_classes]

Returns the log-probabilities of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

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

predict_proba(X)

This function does classification or regression on a test vector X given a model with probability information.

Parameters :

X : scipy.sparse.csr, shape = [n_samples, n_features]

Returns :

X : array-like, shape = [n_samples, n_classes]

Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.

Notes

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

score(X, y)

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0, lower values are worse.

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :