diff --git a/examples/grocery_sales/python/00_quick_start/autoarima_single_round.ipynb b/examples/grocery_sales/python/00_quick_start/autoarima_single_round.ipynb index d260b0b7..f15413e0 100644 --- a/examples/grocery_sales/python/00_quick_start/autoarima_single_round.ipynb +++ b/examples/grocery_sales/python/00_quick_start/autoarima_single_round.ipynb @@ -1131,7 +1131,7 @@ "```\n", "on your local machine before accessing the dashboard locally. Below is a snapshot of the Ray dashboard during a previous run of the notebook.\n", "\n", - "" + "\n" ] }, { diff --git a/examples/grocery_sales/python/02_model/lightgbm_multi_round.ipynb b/examples/grocery_sales/python/02_model/lightgbm_multi_round.ipynb index d061b1ac..f566a571 100644 --- a/examples/grocery_sales/python/02_model/lightgbm_multi_round.ipynb +++ b/examples/grocery_sales/python/02_model/lightgbm_multi_round.ipynb @@ -302,9 +302,9 @@ "source": [ "## Model Training\n", "\n", - "We then perform a multi-round training by fitting a LightGBM model using the training data in each forecast round. After the models are trained, we apply them to generate forecasts for the target weeks. The paradigm of model training and evaluation is illustrated in the following figure\n", + "We then perform a multi-round training by fitting a LightGBM model using the training data in each forecast round. After the models are trained, we apply them to generate forecasts for the target weeks. The paradigm of model training and testing is illustrated in the following diagram\n", "\n", - "" + "" ] }, {