## About final models {: #about-final-models }

The original ("final") model is trained without holdout data and therefore does not have the most recent data. Instead, it represents the first backtest. This is so that predictions match the insights, coefficients, and other data displayed in the tabs that help evaluate models. (You can verify this by checking the **Final model** representation on the **New Training Period** dialog to view the data your model will use.) If you want to use more recent data, retrain the model using [start and end dates](#start-end).


!!! note
	Be careful retraining on all your data. In Time Series it is very common for historical data to have a negative impact on current predictions. There are a lot of good reasons not to retrain a model for deployment on 100% of the data. Think through how the training window can impact your deployments and ask yourself:

	* "Is all of my data actually relevant to my recent predictions?
	* Are there historical changes or events in my data which may negatively affect how current predictions are made, and that are no longer relevant?"
	* Is anything outside my Backtest 1 training window size _actually_ relevant?

## Retrain before deployment {: #retrain-before-deployment }

Once you have selected a model and unlocked holdout, you may want to retrain the model (although with hyperparameters frozen) to ensure predictive accuracy. Because the original model is trained without the holdout data, it therefore did not have the most recent data. You can verify this by checking the **Final model** representation on the **New Training Period** dialog to view the data your model will use.


To retrain the model, do the following:

1. On the Leaderboard, click the plus sign (**+**) to open the **New Training Period** dialog and change the training period.

2. View the final model and determine whether your model is trained on the most up-to-date data.

3. Enable **Frozen** run by clicking the slider.

4. Select **Start/End Date** and enter the dates for the retraining, including the dates of the holdout data. Remember to use the “+1” method (enter the date immediately after the final date you want to be included).

### Model retraining {: #model-retraining }

Retraining a model on the most recent data* results in the model not having [out-of-sample predictions](data-partitioning#what-are-stacked-predictions), which is what many of the Leaderboard insights rely on. That is, the child (recommended and rebuilt) model trained with the most recent data has no additional samples with which to score the retrained model. Because insights are a key component to both understanding DataRobot's recommendation and facilitating model performance analysis, DataRobot links insights from the parent (original) model to the child (frozen) model.

![](images/otp-child-link.png)


\* This situation is also possible when a model is trained into holdout ("slim-run" models also have no [stacked predictions](data-partitioning#what-are-stacked-predictions)).

The insights affected are:

* ROC Curve
* Lift Chart
* Confusion Matrix
* Stability
* Forecast Accuracy
* Series Insights
* Accuracy Over Time
* Feature Effect
