GaussianProcessRegressor
- class desdeo_problem.surrogatemodels.GaussianProcessRegressor(**kwargs)[source]
Bases:
GaussianProcessRegressor,BaseRegressorMethods Summary
predict(X)Predict using the Gaussian process regression model.
Methods Documentation
- predict(X)[source]
Predict using the Gaussian process regression model.
We can also predict based on an unfitted model by using the GP prior. In addition to the mean of the predictive distribution, optionally also returns its standard deviation (return_std=True) or covariance (return_cov=True). Note that at most one of the two can be requested.
- Parameters:
X (array-like of shape (n_samples, n_features) or list of object) – Query points where the GP is evaluated.
return_std (bool, default=False) – If True, the standard-deviation of the predictive distribution at the query points is returned along with the mean.
return_cov (bool, default=False) – If True, the covariance of the joint predictive distribution at the query points is returned along with the mean.
- Returns:
y_mean (ndarray of shape (n_samples,) or (n_samples, n_targets)) – Mean of predictive distribution a query points.
y_std (ndarray of shape (n_samples,) or (n_samples, n_targets), optional) – Standard deviation of predictive distribution at query points. Only returned when return_std is True.
y_cov (ndarray of shape (n_samples, n_samples) or (n_samples, n_samples, n_targets), optional) – Covariance of joint predictive distribution a query points. Only returned when return_cov is True.