DOC Resolve confusion about the role of alpha in Gaussian Process Regression
@@ -48,16 +48,17 @@ class GaussianProcessRegressor(MultiOutputMixin,
passed, the kernel "1.0 * RBF(1.0)" is used as default. Note that
the kernel's hyperparameters are optimized during fitting.
- alpha : float or array-like of shape (n_samples), default=1e-10
+ alpha : float or ndarray of shape (n_samples,), default=1e-10
Value added to the diagonal of the kernel matrix during fitting.
- Larger values correspond to increased noise level in the observations.
- This can also prevent a potential numerical issue during fitting, by
+ This can prevent a potential numerical issue during fitting, by
ensuring that the calculated values form a positive definite matrix.
- If an array is passed, it must have the same number of entries as the
- data used for fitting and is used as datapoint-dependent noise level.
- Note that this is equivalent to adding a WhiteKernel with c=alpha.
- Allowing to specify the noise level directly as a parameter is mainly
- for convenience and for consistency with Ridge.
+ It can also be interpreted as the variance of additional Gaussian
+ measurement noise on the training observations. Note that this is
+ different from using a `WhiteKernel`. If an array is passed, it must
+ have the same number of entries as the data used for fitting and is
+ used as datapoint-dependent noise level. Allowing to specify the
+ noise level directly as a parameter is mainly for convenience and
+ for consistency with Ridge.
optimizer : "fmin_l_bfgs_b" or callable, default="fmin_l_bfgs_b"
Can either be one of the internally supported optimizers for optimizing
