index bugfixing

Author: trevorcampbell

source/classification2.md

@@ -1180,7 +1180,9 @@ functionality, named `GridSearchCV`, to automatically handle the details for us.
 Before we use `GridSearchCV`, we need to create a new pipeline
 with a `KNeighborsClassifier` that has the number of neighbors left unspecified.
 
-```{index} make_pipeline
+```{index} see: make_pipeline; scikit-learn
+```
+```{index} scikit-learn;make_pipeline
 ```
 
 ```{code-cell} ipython3
@@ -1670,7 +1672,7 @@ Overview of K-NN classification.
 
 +++
 
-```{index} scikit-learn, Pipeline, cross-validation, K-nearest neighbors; classification, classification
+```{index} scikit-learn;Pipeline, cross-validation, K-nearest neighbors; classification, classification
 ```
 
 The overall workflow for performing K-nearest neighbors classification using `scikit-learn` is as follows:

source/reading.md

@@ -685,7 +685,10 @@ canlang_table = conn.table("can_lang")
 canlang_table
 ```
 
-```{index} count, ibis; count
+```{index} ibis; count
+```
+
+```{index} see: count; ibis
 ```
 
 Although it looks like we might have obtained the whole data frame from the database, we didn't!
@@ -721,7 +724,9 @@ the *actual* text of the SQL query that `ibis` sends to the database, you can us
 instead of `execute`. But note that you have to pass the result of `compile` to the `str` function to turn it into
 a human-readable string first.
 
-```{index} compile, ibis; compile, str
+```{index} see: compile;ibis
+```
+```{index} ibis; compile, str
 ```
 
 ```{code-cell} ipython3

source/regression1.md

@@ -220,7 +220,7 @@ predict the former.
 
 ## K-nearest neighbors regression
 
-```{index} K-nearest neighbors; regression
+```{index} K-nearest neighbors, K-nearest neighbors; regression
 ```
 
 Much like in the case of classification,
@@ -540,7 +540,7 @@ training or testing data. But many people just use RMSE for both,
 and rely on context to denote which data the root mean squared error is being calculated on.
 ```
 
-```{index} scikit-learn, scikit-learn; pipeline, scikit-learn; make_pipeline, scikit-learn; make_column_transformer
+```{index} scikit-learn, scikit-learn; Pipeline, scikit-learn; make_pipeline, scikit-learn; make_column_transformer
 ```
 
 Now that we know how we can assess how well our model predicts a numerical

source/regression2.md

@@ -734,7 +734,11 @@ mlm.fit(
 ```
 Finally, we make predictions on the test data set to assess the quality of our model.
 
-```{index} predict, mean_squared_error
+```{index} scikit-learn;predict, scikit-learn;mean_squared_error
+```
+```{index} see: mean_squared_error;scikit-learn
+```
+```{index} see: predict;scikit-learn
 ```
 
 ```{code-cell} ipython3