class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, optimizer=’fmin_l_bfgs_b’, n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class=’one_vs_rest’, n_jobs=None)
[source]
Gaussian process classification (GPC) based on Laplace approximation.
The implementation is based on Algorithm 3.1, 3.2, and 5.1 of Gaussian Processes for Machine Learning (GPML) by Rasmussen and Williams.
Internally, the Laplace approximation is used for approximating the nonGaussian posterior by a Gaussian.
Currently, the implementation is restricted to using the logistic link function. For multiclass classification, several binary oneversus rest classifiers are fitted. Note that this class thus does not implement a true multiclass Laplace approximation.
Parameters: 


Attributes: 

>>> from sklearn.datasets import load_iris >>> from sklearn.gaussian_process import GaussianProcessClassifier >>> from sklearn.gaussian_process.kernels import RBF >>> X, y = load_iris(return_X_y=True) >>> kernel = 1.0 * RBF(1.0) >>> gpc = GaussianProcessClassifier(kernel=kernel, ... random_state=0).fit(X, y) >>> gpc.score(X, y) 0.9866... >>> gpc.predict_proba(X[:2,:]) array([[0.83548752, 0.03228706, 0.13222543], [0.79064206, 0.06525643, 0.14410151]])
New in version 0.18.
fit (X, y)  Fit Gaussian process classification model 
get_params ([deep])  Get parameters for this estimator. 
log_marginal_likelihood ([theta, eval_gradient])  Returns logmarginal likelihood of theta for training data. 
predict (X)  Perform classification on an array of test vectors X. 
predict_proba (X)  Return probability estimates for the test vector X. 
score (X, y[, sample_weight])  Returns the mean accuracy on the given test data and labels. 
set_params (**params)  Set the parameters of this estimator. 
__init__(kernel=None, optimizer=’fmin_l_bfgs_b’, n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class=’one_vs_rest’, n_jobs=None)
[source]
fit(X, y)
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Fit Gaussian process classification model
Parameters: 


Returns: 

get_params(deep=True)
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Get parameters for this estimator.
Parameters: 


Returns: 

log_marginal_likelihood(theta=None, eval_gradient=False)
[source]
Returns logmarginal likelihood of theta for training data.
In the case of multiclass classification, the mean logmarginal likelihood of the oneversusrest classifiers are returned.
Parameters: 


Returns: 

predict(X)
[source]
Perform classification on an array of test vectors X.
Parameters: 


Returns: 

predict_proba(X)
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Return probability estimates for the test vector X.
Parameters: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the mean accuracy on the given test data and labels.
In multilabel 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: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


sklearn.gaussian_process.GaussianProcessClassifier
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessClassifier.html