# Manage experiments
URL: https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/index.html

> Provides access to the data insights and the model Leaderboard where you can evaluate forecasting e models.

# Manage experiments

After modeling has started, DataRobot constructs a model Leaderboard to help learn and understand models and the data that built them. Also from the Leaderboard you can [add or retrain models](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/experiment-add.html) and [create custom blueprints](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/experiment-cml.html) from the Leaderboard blueprint using built-in tasks and custom Python/R code.

Tiles on the left-side of the experiment Leaderboard provide all the tools necessary for managing predictive experiments. They are described in the following section:

| Tile | Name | Description |
| --- | --- | --- |
|  | Experiment setup | Opens the experiment setup summary page. |
|  | Data preview | Displays a more visual representation of the features in your dataset, including frequent values. |
|  | Features | Displays features in a table format alongside feature importance and summary statistics. Select specific features to view more detailed data insights than those shown on the Data preview tile. |
|  | Feature lists | Allows you to create new feature lists, manage existing ones, and retrain all the models in an experiment on a different feature list. |
|  | Data insights | Helps you track and visualize associations within your data using the Feature Associations insight. |
|  | Blueprint repository | Opens library of modeling blueprints available for a selected experiment. |
|  | Model Leaderboard | Opens a list of all built models and overview information for each. Provides access to the model's available insights. |
|  | Experiment insights | Opens experiment-level insights for all models. |
|  | Model comparison | Opens a tool for comparing compatible models within or across experiments. Model comparison is not available for time-aware experiments but for Use Cases with non-time-aware experiments, you can initiate a compare from within a time-aware experiment. |

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