Fixed broken doc links

docs/guides/general_concepts.rst

@@ -3,6 +3,7 @@ General Concepts
 
 Parameters
 ----------
+.. _parameters:
 
 Parameters define the behavior of an algorithm or a pipeline.
 They are knobs you can turn to make the analysis behave like you want to.

docs/guides/optimization.rst

@@ -8,7 +8,7 @@ of thresholds in classical algorithms.
 Therefore, we attempt to have a unified interface for all these cases.
 
 This is achieved by defining "optimization" as any form of data-driven optimization of the "parameters"
-(see :ref:`parameters`) specified in the `__init__` of an algorithm.
+(see `parameters`_) specified in the `__init__` of an algorithm.
 This optimization can be performed via *internal* optimization implemented in a `self_optimize` method on the pipeline
 or via *external* optimization like the :class:`~tpcp.optimize.GridSearch` wrapper.
 

examples/parameter_optimization/_03_gridsearch_cv.py

@@ -109,8 +109,8 @@ def score(pipeline: MyPipeline, datapoint: ECGExampleData):
 # Data Splitting
 # --------------
 # Like with a normal cross validation, we need to decide on the number of folds and type of splits.
-# In `tpcp` we support all cross validation iterators provided in :ref:`sklearn
-# <https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators>`.
+# In `tpcp` we support all cross validation iterators provided in
+# `sklearn <https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators>`__.
 #
 # To keep the runtime low for this example, we are going to use a 2-fold CV.
 from sklearn.model_selection import KFold

examples/validation/_01_cross_validation.py

@@ -98,8 +98,8 @@ def score(pipeline: MyPipeline, datapoint: ECGExampleData):
 # Data Splitting
 # --------------
 # Before performing a cross validation, we need to decide on the number of folds and type of splits.
-# In `tpcp` we support all cross validation iterators provided in :ref:`sklearn
-# <https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators>`.
+# In `tpcp` we support all cross validation iterators provided in
+# `sklearn <https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators>`__.
 #
 # To keep the runtime low for this example, we are going to use a 3-fold CV.
 from sklearn.model_selection import KFold