# 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.

<figure><img src="/files/GrDWc81OkIiayunfRFuQ" alt=""><figcaption></figcaption></figure>

### 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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