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.
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.
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.
Train vs Validation Performance
X: accuracy
accuracy
Y: val_accuracy
val_accuracy
Radius: epochs
epochs
Detect overfitting or see whether longer training improves validation.
Quality vs Loss
X: val_loss
val_loss
Identify strong performers and weak outliers.
Hyperparameter Sensitivity
X: learning_rate
learning_rate
See which ranges actually improve performance.
Efficiency Trade-off
X: training_time
training_time
Radius: model_size
model_size
Understand whether larger or slower models are worth it.
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
Select executions in the left panel.
Open Metadata view.
Click Scatter Plot.
Choose X, Y (and optional Radius).
Apply filters if needed.
The plot updates instantly.
Last updated 4 months ago
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