DOC small typos and fixes for poisson example (scikit-learn#17078)

Christian Lorentzen · viclafargue · commit 57f1ff7e45d3 · 2020-06-26T11:43:34.000Z
diff --git a/examples/linear_model/plot_poisson_regression_non_normal_loss.py b/examples/linear_model/plot_poisson_regression_non_normal_loss.py
@@ -277,10 +277,9 @@ def score_estimator(estimator, 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 @@ def score_estimator(estimator, df_test):
 #
 # 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 @@ def lorenz_curve(y_true, y_pred, exposure):
 # 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 @@ def lorenz_curve(y_true, y_pred, exposure):
 # - 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.
