Merge pull request #111730 from cartacioS/patch-35

Predict should not be used in forecasting

Author: PRMerger14

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

@@ -208,12 +208,7 @@ fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()
 
 Use the best model iteration to forecast values for the test data set.
 
-```python
-predict_labels = fitted_model.predict(test_data)
-actual_labels = test_labels.flatten()
-```
-
-Alternatively, you can use the `forecast()` function instead of `predict()`, which will allow specifications of when predictions should start. In the following example, you first replace all values in `y_pred` with `NaN`. The forecast origin will be at the end of training data in this case, as it would normally be when using `predict()`. However, if you replaced only the second half of `y_pred` with `NaN`, the function would leave the numerical values in the first half unmodified, but forecast the `NaN` values in the second half. The function returns both the forecasted values and the aligned features.
+The `forecast()` function should be used instead of `predict()`, this will allow specifications of when predictions should start. In the following example, you first replace all values in `y_pred` with `NaN`. The forecast origin will be at the end of training data in this case, as it would normally be when using `predict()`. However, if you replaced only the second half of `y_pred` with `NaN`, the function would leave the numerical values in the first half unmodified, but forecast the `NaN` values in the second half. The function returns both the forecasted values and the aligned features.
 
 You can also use the `forecast_destination` parameter in the `forecast()` function to forecast values up until a specified date.