Create experiments¶
Experiments are the individual "projects" within a Use Case. They allow you to vary data, targets, and modeling settings to find the optimal models to solve your business problem. Within each experiment, you have access to its Leaderboard and model insights, as well as information that summarizes the data and experiment setup.
The following sections help to understand building two types of AI experiments machine learning experiments in Workbench:
| Topic | Describes |
|---|---|
| Create predictive experiments | Build supervised or unsupervised experiments and fine-tune experiment setup, making row-by-row predictions based on your data. |
| Create time-aware experiments | Build supervised or unsupervised experiments and fine-tune experiment setup, using time-relevant data to make row-by-row predictions, time series forecasts, or current value predictions "nowcasts". |
Note
An experiment can only be a part of a single Use Case. The reason for this is because a Use Case is intended to represent a specific business problem and experiments within the Use Case are typically directed at solving that problem. If an experiment is relevant for more than one Use Case, consider consolidating the two Use Cases.