8 posts tagged with "Visualization"

See how each completed trial moves through its parameters and targets in a single parallel coordinates chart.

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What's included​

Parallel coordinates​

• Open Parallel coordinates from an experiment's Visualization page
• Each completed trial is drawn as one line, running across every parameter on the left and the selected targets on the right
• Every axis has its own scale, so parameters and targets with different units can be compared side by side
• Categorical parameters show one tick per category
• Hover a line to see that trial's values on every axis

Reorder axes​

• Drag an axis title left or right to move that axis
• Place related parameters next to each other, or move a parameter next to a target to see how they relate

Color by target​

• Lines are colored by the last selected target, so you can follow where trials end up
• Drag any target from the sidebar onto Color to color by it instead
• A target dropped onto Color is added to the chart if it is not already selected
• Use the color scale slider to show only trials within a range of target values
• Clear Color to show every line in a single color

Overlapping trials​

• Trials that share the same value on an axis are spread slightly apart, so each one stays visible
• Trials with unique values stay exactly on their tick

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Notes​

• Only completed trials with a value on every axis are shown
• Axis order and color choice reset when you leave the Parallel coordinates tab

Follow optimization progress more clearly with completed results ordered as observations and predictions grouped separately.

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What's included​

A clearer optimization sequence​

• Completed target results are numbered in completion order, without gaps from trials that did not produce a measurement
• The original trial number and completion time remain available in the point details
• Predictions for Pending and Accepted trials appear after the completed observations
• A dashed divider separates measured results from predictions across selected targets

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Notes​

• The observation number can differ from the original trial number when trials finish out of order or do not produce a target result
• Predictions remain model estimates and include error bars showing one posterior standard deviation above and below the mean

Explore how each parameter influences experiment targets with SHAP-based importance visualizations.

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What's included​

Parameter importance​

• Open Parameter importance from an experiment's Visualization page
• Select a target to inspect how its parameters contribute to the surrogate model output
• Compare parameters using their mean absolute SHAP values
• See the direction and distribution of each parameter's impact in the beeswarm plot
• Use the feature-value scale to focus on observations with low or high numeric parameter values

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Notes​

• Parameter importance requires enough completed measurements to fit the surrogate model
• SHAP values describe model behavior and do not imply that a parameter causes a target outcome

View BayBE target predictions and their uncertainty for Pending and Accepted trials.

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What's included​

Predictions in trial cards​

• Pending and Accepted trial cards show each target's predicted mean and posterior standard deviation as mean ± std
• Predictions update as completed trial results are added to the experiment

Predictions in Convergence​

• Prediction points appear alongside completed measurements on the same target axis
• Error bars extend from mean − std to mean + std to show model uncertainty
• Hover over a prediction to inspect its value and error-bar bounds
• Use Error bar opacity (%) to adjust the visibility of the error bars, including hiding them at 0%

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Notes​

• Predictions are model estimates and are not measured target results
• Error-bar bounds are not observed minimum and maximum values or a confidence interval

Instantly understand any plot with an AI-generated summary — powered by an LLM with vision.

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What's included​

How it works​

• Click the AI Summary button in the Plot Builder toolbar
• A vision-enabled LLM reads the plot statistics and sees the chart, then returns bullet points covering distributions, group breakdowns, data quality issues, and visual patterns like clusters or outliers
• Hit copy to paste the summary into a report or analysis note

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Notes​

• Works with scatter plots, box plots, and heatmaps
• The summary is factual only — the LLM does not assess or make recommendations
• If you notice any unexpected behavior, please share steps to reproduce

New desirability convergence chart for tracking optimization progress across experiment trials.

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What's included​

How it works​

• Select a numerical parameter from the parameter sidebar to color data points by that parameter's value
• A continuous color scale shows how parameter values relate to desirability scores across trials
• The best so far line tracks the highest desirability achieved up to each trial

• Select a categorical parameter to color data points by category
• A discrete color legend identifies each category value

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Notes​

• The convergence chart is available under the Desirability tab in the Visualization section
• Chart interactions and layout may continue to evolve during the beta phase
• If you notice any unexpected behavior, please share steps to reproduce

Split convergence plot into a multi-panel grid when multiple targets are selected.

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What's included​

How it works​

• Select multiple targets from the target sidebar
• The convergence plot automatically splits into a grid — one panel per target
• Each panel shares the same x-axis (iteration index) but has an independent y-axis scale

• Click a data point to highlight the corresponding trial across all panels

• Shift-drag to draw a brush selection across panels
• Points outside the selection are dimmed
• Up to 6 targets can be displayed at a time

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Notes​

• Single target selection continues to show the standard convergence plot
• Trellis layout and interactions may continue to evolve during the beta phase
• If you notice any unexpected behavior, please share steps to reproduce

Split scatter plots and box plots into a multi-panel grid by any categorical column to compare patterns across groups side by side.

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What's included​

How it works​

• Drag a categorical column onto the Trellis drop zone in the plot sidebar
• The plot splits into a grid of panels — one per category
• Works with scatter plots and box plots

• Use the trellis legend to choose which categories to display
• Up to 4 panels can be shown at a time

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Notes​

• Heatmaps do not support trellis
• Trellis behavior may continue to evolve during the beta phase
• If you notice any unexpected behavior, please share steps to reproduce