fix: broken links fixed

Author: cosmic-cortex

source/content/examples/Active-regression.rst

@@ -20,7 +20,7 @@ For this example, we shall try to learn the *noisy sine* function:
 Uncertainty measure and query strategy for Gaussian processes
 -------------------------------------------------------------
 
-For active learning, we shall define a custom query strategy tailored to Gaussian processes. More information on how to write your custom query strategies can be found at the page `Extending modAL <../overview/Extending-modAL>`__. In a nutshell, a *query stategy* in modAL is a function taking (at least) two arguments (an estimator object and a pool of examples), outputting the index of the queried instance and the instance itself. In our case, the arguments are ``regressor`` and ``X``.
+For active learning, we shall define a custom query strategy tailored to Gaussian processes. More information on how to write your custom query strategies can be found at the page `Extending modAL <../overview/Extending-modAL.html>`__. In a nutshell, a *query stategy* in modAL is a function taking (at least) two arguments (an estimator object and a pool of examples), outputting the index of the queried instance and the instance itself. In our case, the arguments are ``regressor`` and ``X``.
 
 .. code:: python
 

source/content/examples/Ensemble-regression.rst

@@ -25,8 +25,7 @@ For the sake of this example, we are going to learn the *noisy absolute value* f
 Measuring disagreement with CommitteeRegression
 -----------------------------------------------
 
-If you have several regressors, measuring disagreement can be done by calculating the standard deviation of the predictions for each point. This of course cannot be achieved with classifier algorithms, where averaging the class labels doesn't make sense. (Or it is undefined even, if the class labels are strings for example.) In the simplest setting, this is implemented in the function
-``modAL.disagreement.max_std_sampling``. For more details on this, see the `disagreement sampling API reference <Disagreement-sampling-API>`__.
+If you have several regressors, measuring disagreement can be done by calculating the standard deviation of the predictions for each point. This of course cannot be achieved with classifier algorithms, where averaging the class labels doesn't make sense. (Or it is undefined even, if the class labels are strings for example.) In the simplest setting, this is implemented in the function ``modAL.disagreement.max_std_sampling``.
 
 This measure is default for CommitteeRegressors, so we don't need to specify this upon initialization.