scikit-learn
diff --git a/‎examples/compose/plot_column_transformer_mixed_types.py
Copy file name to clipboardExpand all lines: examples/compose/plot_column_transformer_mixed_types.py
+3-3Lines changed: 3 additions & 3 deletions b/‎examples/compose/plot_column_transformer_mixed_types.py
Copy file name to clipboardExpand all lines: examples/compose/plot_column_transformer_mixed_types.py
+3-3Lines changed: 3 additions & 3 deletions
@@ -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)))