The Grouped Plot lets you compare distributions across experiment groups.
Instead of comparing single values, you compare full distributions using box plots and summary statistics.
Each row represents a group of selected executions.
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 grouped comparison.
In the right panel, configure:
Choose a categorical metadata key. Each unique value becomes a row.
Examples:
dataset_name
model_version
instance_type
architecture
Select one or more numeric metadata fields to analyze.
val_accuracy
val_loss
training_time
latency
Each selected value becomes a column with its own box plot and statistics.
You can toggle which statistics to display:
n (number of executions)
Min
Q1
Median
Q3
Max
Mean
You can also:
Show / hide box plot
Show / hide individual observations
Compare Datasets
Group by: dataset_name
Values: val_accuracy, val_loss
See which dataset performs best and which introduces more variance.
Compare Model Versions
Group by: model_version
Value: val_accuracy
Understand which version is most stable and which has higher median performance.
Infrastructure Benchmark
Group by: instance_type
Value: training_time
Detect variability or long-tail performance differences.
Use it when you want to:
Compare distributions (not just averages)
Detect instability within experiment groups
Identify variance patterns
Evaluate A/B tests or multi-variant experiments
Benchmark datasets, architectures, or infrastructure
It's especially useful when you care about consistency, not just peak performance.
Select executions in the left panel.
Open Metadata view.
Click Grouped Plot.
Choose:
Group By key
One or more Values
Visible aggregates (optional)
Apply filters if needed.
The table and box plots update instantly.
Last updated 4 months ago
Was this helpful?
