make new paragraph

Author: EricWrightAtWork

articles/machine-learning/how-to-auto-train-forecast.md

@@ -300,7 +300,9 @@ Use the best model iteration to forecast values for data that wasn't used to tra
 
 Before you put a model into production, you should evaluate its accuracy on a test set held out from the training data. A best practice procedure is a so-called rolling evaluation which rolls the trained forecaster forward in time over the test set, averaging error metrics over several prediction windows to obtain statistically robust estimates for some set of chosen metrics. Ideally, the test set for the evaluation is long relative to the model's forecast horizon. Estimates of forecasting error may otherwise be statistically noisy and, therefore, less reliable.
 
-For example, suppose you train a model on historic, daily sales to predict demand up to two weeks (14 days) into the future. If there is sufficient historic data available, you might reserve the final several months to even a year of the data for the test set. The rolling evaluation begins by generating a 14-day-ahead forecast for the first two weeks of the test set. Then, the forecaster is advanced by some number of days into the test set and you generate another 14-day-ahead forecast from the new position. The process continues until you get to the end of the test set. To do a rolling evaluation, you call the `rolling_forecast` method of the `fitted_model`, then compute desired metrics on the result. For example, assume you have test set features in a pandas DataFrame called `test_features_df` and the test set actual values of the target in a numpy array called `test_target`. A rolling evaluation using the mean squared error is shown in the following code sample:
+For example, suppose you train a model on historic, daily sales to predict demand up to two weeks (14 days) into the future. If there is sufficient historic data available, you might reserve the final several months to even a year of the data for the test set. The rolling evaluation begins by generating a 14-day-ahead forecast for the first two weeks of the test set. Then, the forecaster is advanced by some number of days into the test set and you generate another 14-day-ahead forecast from the new position. The process continues until you get to the end of the test set.
+
+To do a rolling evaluation, you call the `rolling_forecast` method of the `fitted_model`, then compute desired metrics on the result. For example, assume you have test set features in a pandas DataFrame called `test_features_df` and the test set actual values of the target in a numpy array called `test_target`. A rolling evaluation using the mean squared error is shown in the following code sample:
 
 ```python
 from sklearn.metrics import mean_squared_error