DOC Fix typo in Gaussian Process docs
@@ -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
