EDA (exploratory data analysis) is a quick sanity check on your data before you commit a training run to it. It has nothing to configure — connect it and it runs immediately, in the time it takes the page to load, rather than as a background job.
Connecting it
Drag an EDA node off Period (or directly off Variables to use the full date range). There's no modal to fill in — the connection itself is the configuration.
What it shows
| Tab | What it's for |
|---|---|
| VIF (Variance Inflation Factor) | Flags channels that move together closely enough to confuse a model about which one is actually driving the outcome. A high VIF on two channels is a warning that the model may struggle to separate their individual contributions. |
| Correlation matrix | How every pair of mapped columns correlates with each other and with your output. |
| Time series | Your output and each channel plotted over the period, so you can spot an obvious data problem (a gap, a flat line, a spike) before training. |
| Media cost share | How your budget is actually split across channels over the window — useful for noticing a channel that's too small to model reliably. |
| Lagged correlation vs. output | Whether a channel's effect on the outcome shows up with a delay, which is a hint about how much adstock (carryover) that channel might need. |
Why run this before a model
Nothing here stops you from training anyway, but a few minutes here can save a much longer wait: two heavily collinear channels, a channel with almost no spend variation, or a data gap are all things that make a model's results hard to trust even when training finishes without error. EDA exists to catch those before you spend a training run on them.
What connects here
Upstream: Period or Variables. EDA doesn't have an output — it's a dead end on the canvas, not a step other nodes build on.