gaussian_process.GaussianProcessRegressor()

sklearn.gaussian_process.GaussianProcessRegressor

class sklearn.gaussian_process.GaussianProcessRegressor(kernel=None, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None) [source]

Gaussian process regression (GPR).

The implementation is based on Algorithm 2.1 of Gaussian Processes for Machine Learning (GPML) by Rasmussen and Williams.

In addition to standard scikit-learn estimator API, GaussianProcessRegressor:

• allows prediction without prior fitting (based on the GP prior)
• provides an additio
  登录查看完整内容