Fix class links

Author: opcode81

docs/1-supervised-learning/1-vector-models.ipynb

@@ -444,14 +444,14 @@
     "\n",
     "sensAI provides a wide variety of base classes that simplify the definition of feature generators, including\n",
     "\n",
-    " * [FeatureGeneratorTakeColumns](/sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorTakeColumns), which simply takes over columns from the input data frame without modifying them\n",
-    " * [FeatureGeneratorMapColumn](sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorMapColumn), which maps the values of an input column to a new feature column\n",
-    " * [FeatureGeneratorFlattenColumns](sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorFlattenColumns), which generates features by flattening one or more vector-valued columns in the input\n",
-    " * [FeatureGeneratorMapColumnDict](sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorMapColumnDict), which maps an input column to several feature columns, i.e. mapping each input value to a dictionary of output values\n",
-    " * [FeatureGeneratorFromVectorModel](sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorFromVectorModel), which generates features by applying a (regression or classifcation) model to the input data frame\n",
-    " * [FeatureGeneratorFromDFT](sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorFromDFT), which generates features by applying a given data frame transformer to the input\n",
-    " * [FeatureGeneratorFromColumnGenerator](sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorFromColumnGenerator), which uses the concept of a `ColumnGenerator` to implement a feature generator, which specifically supports index-based caching mechanisms for feature generation\n",
-    " * [FeatureGeneratorTargetDistribution](sensAI/docs/sensai/featuregen/feature_generator.html#sensai.featuregen.feature_generator.FeatureGeneratorTargetDistribution), which computes conditional distributions of the (optionally discretised) target variable given one or more categorical features in the input data\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorTakeColumns`, which simply takes over columns from the input data frame without modifying them\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorMapColumn`, which maps the values of an input column to a new feature column\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorFlattenColumns`, which generates features by flattening one or more vector-valued columns in the input\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorMapColumnDict`, which maps an input column to several feature columns, i.e. mapping each input value to a dictionary of output values\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorFromVectorModel`, which generates features by applying a (regression or classifcation) model to the input data frame\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorFromDFT`, which generates features by applying a given data frame transformer to the input\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorFromColumnGenerator`, which uses the concept of a `ColumnGenerator` to implement a feature generator, which specifically supports index-based caching mechanisms for feature generation\n",
+    " * :class:`sensai.featuregen.feature_generator.FeatureGeneratorTargetDistribution`, which computes conditional distributions of the (optionally discretised) target variable given one or more categorical features in the input data\n",
     "\n",
     "As a simple example, let us use `FeatureGeneratorMapColumn` to implement a second feature based on the timestamp column from the earlier data frame: the day of the week."
    ]