From 77b40d8cd2b75670ee33ceacb6f7ed2bb7be3793 Mon Sep 17 00:00:00 2001 From: "Adam J. Stewart" Date: Fri, 18 Dec 2020 15:02:37 -0600 Subject: [PATCH] [DOC] Fix typo in Gaussian Process docs --- doc/modules/gaussian_process.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/modules/gaussian_process.rst b/doc/modules/gaussian_process.rst index 040c3c4f9adf5..6aa9cb417aa5d 100644 --- a/doc/modules/gaussian_process.rst +++ b/doc/modules/gaussian_process.rst @@ -156,9 +156,9 @@ required for fitting and predicting: while fitting KRR is fast in principle, the grid-search for hyperparameter optimization scales exponentially with the number of hyperparameters ("curse of dimensionality"). The gradient-based optimization of the parameters in GPR does not suffer from this exponential -scaling and is thus considerable faster on this example with 3-dimensional +scaling and is thus considerably faster on this example with 3-dimensional hyperparameter space. The time for predicting is similar; however, generating -the variance of the predictive distribution of GPR takes considerable longer +the variance of the predictive distribution of GPR takes considerably longer than just predicting the mean. GPR on Mauna Loa CO2 data @@ -294,7 +294,7 @@ with different choices of the hyperparameters. The first figure shows the predicted probability of GPC with arbitrarily chosen hyperparameters and with the hyperparameters corresponding to the maximum log-marginal-likelihood (LML). -While the hyperparameters chosen by optimizing LML have a considerable larger +While the hyperparameters chosen by optimizing LML have a considerably larger LML, they perform slightly worse according to the log-loss on test data. The figure shows that this is because they exhibit a steep change of the class probabilities at the class boundaries (which is good) but have predicted @@ -384,7 +384,7 @@ equivalent call to ``__call__``: ``np.diag(k(X, X)) == k.diag(X)`` Kernels are parameterized by a vector :math:`\theta` of hyperparameters. These hyperparameters can for instance control length-scales or periodicity of a -kernel (see below). All kernels support computing analytic gradients +kernel (see below). All kernels support computing analytic gradients of the kernel's auto-covariance with respect to :math:`log(\theta)` via setting ``eval_gradient=True`` in the ``__call__`` method. That is, a ``(len(X), len(X), len(theta))`` array is returned where the entry