8000 DOC Fix typo in Gaussian Process docs by adamjstewart · Pull Request #19039 · scikit-learn/scikit-learn · GitHub
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DOC Fix typo in Gaussian Process docs #19039

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Dec 20, 2020
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DOC Fix typo in Gaussian Process docs #19039

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8 changes: 4 additions & 4 deletions doc/modules/gaussian_process.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
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