> For the complete documentation index, see [llms.txt](https://docs.valohai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.valohai.com/experiment-tracking/visualize-metrics/scatter-plot.md).
# Scatter Plot
The Scatter Plot helps you understand relationships between experiment metrics.
Instead of looking at one metric at a time, you can compare two (or three) at once and immediately see patterns, trade-offs, and outliers.
Each point represents one execution.
### Selecting Executions
Before configuring the plot, choose which executions you want to compare.
On the **left side panel**, you can:
* Add executions to the comparison
* Remove executions
* Search and filter runs
Only the selected executions appear in the scatter plot.
### Define Your Plot
In the right panel, choose:
* **X Axis** - Any numeric metadata or parameter
* **Y Axis** - Another numeric metadata field
* **Radius (optional)** - A third numeric value that controls bubble size
If Radius is selected, points become bubbles. Larger values = larger circles.
#### Common Plot Setups
**Train vs Validation Performance**
* X: `accuracy`
* Y: `val_accuracy`
* Radius: `epochs`
Detect overfitting or see whether longer training improves validation.
**Quality vs Loss**
* X: `val_loss`
* Y: `val_accuracy`
Identify strong performers and weak outliers.
**Hyperparameter Sensitivity**
* X: `learning_rate`
* Y: `val_accuracy`
See which ranges actually improve performance.
**Efficiency Trade-off**
* X: `training_time`
* Y: `val_accuracy`
* Radius: `model_size`
Understand whether larger or slower models are worth it.
### Why Use Scatter Plot?
Use it when you want to:
* Detect correlations between metrics
* Spot clusters of similar runs
* Identify outliers
* Understand trade-offs (quality vs speed, size vs accuracy)
Especially useful for:
* Hyperparameter sweeps
* Architecture comparisons
* Benchmarking experiments
### How To Use It
1. Select executions in the left panel.
2. Open **Metadata view**.
3. Click **Scatter Plot**.
4. Choose X, Y (and optional Radius).
5. Apply filters if needed.
The plot updates instantly.
---
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