# Model Iterations
URL: https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/incremental.html

> View the insight that compares the accuracy of trained iterations and, optionally, change the active iteration.

# Model Iterations

| Tab | Description |
| --- | --- |
| Details | Allows you to compare trained iterations and, optionally, assign a different active iteration or continue training. |

Model Iterations compares trained iterations of models built using [incremental learning (IL)](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-adv-experiment.html#configure-incremental-learning), a model training method specifically tailored for large datasets. The method chunks data and creates training iterations, which you can compare and optionally assign a different active version or continue training from this tab. The active iteration is the basis for other insights and is used for making predictions.

The active iteration—the iteration of the model that has the highest accuracy—is the basis for other Leaderboard insights and is used for making predictions. As a result, the Leaderboard for an IL experiment does not show all models; instead, it shows a representative of each model. As training continues, the best model is assigned as the active model at the end of each iterative round.

Identifying information is listed on the Leaderboard model card:

And the Model Overview, which also indicates which iteration is the active iteration for that model:

## Iteration learning curve

The Model Iterations insight provides a learning curve to help visualize the validation score for each iteration. Validation score is the basis of the active iteration selection. The chart shows all trained iterations and, when iteration training is in progress, updates in real time so that you can monitor training progress on each chunk. Hover on any point in the curve to see the validation score and cumulative rows for that iteration.

When there are more than 100 iterations, a preview is shown below the learning curve chart. The preview controls which 100 iterations to display. Once the preview chart becomes available, it also updates as training continues. Use the handles on the preview to drag the window and change the view.

The active iteration is marked with a green circle. Note that, depending on the view selected from the preview, the active iteration may not be visible.

## Available actions

Click View experiment info to view details of the built experiment, including total rows and iterations:

For each model built, you can potentially:

- Change the active iteration
- Train remaining iterations

Options may be unavailable or not present if they are not applicable. For example, if all iterations were run, the option to train any remaining iterations is unavailable. However, if only one iteration ran, the option to change the active iteration is present but unavailable until you train the remaining iterations.

### Change active iteration

For models with more than one iteration run, the most accurate iteration is marked with an Active badge. For a selected model, click Change active iteration to select a different iteration:

Selecting a new active iteration updates:

- The score on the model card, and therefore the model ranking.
- The scores in the Training scores section at the top of Model Overview .

### Resume training

If, when configuring the experiment, you selected Stop training when model accuracy no longer improves, any model that was not identified by DataRobot as the most accurate runs only a single iteration. You can, however, train new model iterations on any untrained increments. Click Train remaining iterations to resume training for the selected model. With this selection, DataRobot trains all remaining iterations until early stopping is applied, not just the next increment. Click Train remaining iterations again to train all remaining iterations, overriding the early stopping setting.

Once selected, DataRobot begins training the next iteration, making an entry in the table. Click Stop training at any time to stop the process and save only the completed iterations.

## Understand the table

Each model displays a table containing an iteration log with the following fields:

| Table element | Description |
| --- | --- |
| Model iteration | Number of the model iteration. |
| Status badges | Badges indicating the state of the iteration, either Active, Training in progress, Errored, or Queued for iterations for which training has been requested. |
| Cumulative rows | Total number of rows processed for the iteration. For example, if increment size is set to 1000 rows, increment 3 would represent 1000 + 1000 + 1000 rows, so cumulative rows would be 3000. |
| Accuracy columns | Accuracy metric values for the validation and holdout partitions. The applied metric is indicated in the column header. |

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