Pushing the docs to dev/ for branch: main, commit c59a310e5eb4c3f72a00503a2643005551b9d3eb

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -130,7 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The resulting score is not exactly the same as the one from the previous\npipeline becase the dtype-based selector treats the ``pclass`` columns as\na numeric features instead of a categorical feature as previously:\n\n"
+        "The resulting score is not exactly the same as the one from the previous\npipeline because the dtype-based selector treats the ``pclass`` column as\na numeric feature instead of a categorical feature as previously:\n\n"
       ]
     },
     {
@@ -231,7 +231,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The best hyper-parameters have be used to re-fit a final model on the full\ntraining set. We can evaluate that final model on held out test data that was\nnot used for hyparameter tuning.\n\n\n"
+        "The best hyper-parameters have be used to re-fit a final model on the full\ntraining set. We can evaluate that final model on held out test data that was\nnot used for hyperparameter tuning.\n\n\n"
       ]
     },
     {

dev/_downloads/26f110ad6cff1a8a7c58b1a00d8b8b5a/plot_column_transformer_mixed_types.ipynb

@@ -147,8 +147,8 @@
 
 # %%
 # The resulting score is not exactly the same as the one from the previous
-# pipeline becase the dtype-based selector treats the ``pclass`` columns as
-# a numeric features instead of a categorical feature as previously:
+# pipeline because the dtype-based selector treats the ``pclass`` column as
+# a numeric feature instead of a categorical feature as previously:
 
 selector(dtype_exclude="category")(X_train)
 
@@ -201,7 +201,7 @@
 # %%
 # The best hyper-parameters have be used to re-fit a final model on the full
 # training set. We can evaluate that final model on held out test data that was
-# not used for hyparameter tuning.
+# not used for hyperparameter tuning.
 #
 print(("best logistic regression from grid search: %.3f"
        % grid_search.score(X_test, y_test)))