DOC small typos and fixes for poisson example
@@ -277,10 +277,9 @@ score_estimator(poisson_gbrt, df_test)
##############################################################################
-# Like the Ridge regression above, the gradient boosted trees model minimizes
-# the conditional squared error. However, because of a higher predictive power,
-# it also results in a smaller Poisson deviance than the linear Poisson
-# regression model.
+# Like the Poisson GLM above, the gradient boosted trees model minimizes
+# the Poisson deviance. However, because of a higher predictive power,
+# it reaches lower values of Poisson deviance.
#
# Evaluating models with a single train / test split is prone to random
# fluctuations. If computing resources allow, it should be verified that
@@ -339,7 +338,7 @@ plt.tight_layout()
#
# Note that we could have used the least squares loss for the
# ``HistGradientBoostingRegressor`` model. This would wrongly assume a normal
-# distribution the response variable as for the `Ridge` model, and possibly
+# distributed response variable as does the `Ridge` model, and possibly
# also lead to slightly negative predictions. However the gradient boosted
# trees would still perform relatively well and in particular better than
# ``PoissonRegressor`` thanks to the flexibility of the trees combined with the
@@ -533,13 +532,9 @@ ax.legend(loc="upper left")
# Main takeaways
# --------------
#
-# - The performance of the models can be evaluted by their ability to yield
+# - The performance of the models can be evaluated by their ability to yield
# well-calibrated predictions and a good ranking.
#
-# - The Gini index reflects the ability of a model to rank predictions
-# irrespective of their absolute values, and therefore only assess their
-# ranking power.
-#
# - The calibration of the model can be assessed by plotting the mean observed
# value vs the mean predicted value on groups of test samples binned by
# predicted risk.
@@ -552,6 +547,10 @@ ax.legend(loc="upper left")
# - Using the Poisson loss with a log-link can correct these problems and lead
# to a well-calibrated linear model.
#
+# - The Gini index reflects the ability of a model to rank predictions
+# irrespective of their absolute values, and therefore only assess their
+# ranking power.
+#
# - Despite the improvement in calibration, the ranking power of both linear
# models are comparable and well below the ranking power of the Gradient
# Boosting Regression Trees.
