Formatting fixes in time features example
@@ -559,10 +559,10 @@ _ = ax.legend()
# than an hour. However, we will see in the following that what can be an
# advantage for linear models is not necessarily one for more expressive
# models.
-#
+
+# %%
# We can also compare the number of features extracted by each feature
# engineering pipeline:
-# %%
naive_linear_pipeline[:-1].transform(X).shape
# %%
@@ -591,7 +591,7 @@ cyclic_spline_linear_pipeline[:-1].transform(X).shape
# explained by the lack of non-additive modeling of the interactions between
# features (in this case "workingday" and features derived from "hours"). This
# issue will be addressed in the following section.
-#
+
# %%
# Modeling pairwise interactions with splines and polynomial features
# -------------------------------------------------------------------
@@ -819,4 +819,4 @@ for ax, pred, label in zip(axes, predictions, labels):
# problems, it is likely that using a Poisson or Gamma distribution would make
# more sense. This could be achieved by using
# `GridSearchCV(TweedieRegressor(power=2), param_grid({"alpha": alphas}))`
-# instead of `RidgeCV`
+# instead of `RidgeCV`.
