figure_type
Back to L7 | Browse all axes | Browse all options
Axis
figure_typeon sub-layerL7_B_output_shape_export(layerl7).
Sub-layer
L7_B_output_shape_export
Axis metadata
Default:
'auto'Sweepable: False
Status: operational
Operational status summary
Operational: 6 option(s)
Future: 0 option(s)
Options
auto – operational
Pick the figure type matching the importance op’s default mapping.
Each L7.A op declares its canonical figure type (shap_* → bar/beeswarm; partial_dependence → lineplot; shap_interaction → heatmap; rolling_recompute → heatmap; etc.). Setting figure_type = auto honours that default.
When to use
Default; lets each L7.A op choose the canonical figure for its output.
References
macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Related options: bar, boxplot, heatmap, lineplot, scatter
Last reviewed 2026-05-05 by macroforecast author.
bar – operational
Horizontal bar chart – one bar per feature, length = importance score.
The standard global-importance visualisation. Renders features sorted by mean-|importance| so the most important variables surface at the top of the chart. Pair with output_table_format = wide for direct table-figure cross-reference.
When to use
Global importance rankings (linear coefficients, permutation, mean SHAP).
References
macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Related options: auto, boxplot, heatmap, lineplot, scatter
Last reviewed 2026-05-05 by macroforecast author.
boxplot – operational
Boxplot of per-fold / per-bootstrap importance distributions.
Renders each feature as a box capturing the distribution of its importance score across folds (cross-validation, bootstrap, rolling windows). Reveals stability information that a single bar cannot convey.
When to use
Stability-of-importance audits; bootstrap_jackknife / rolling_recompute outputs.
References
macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Related options: auto, bar, heatmap, lineplot, scatter
Last reviewed 2026-05-05 by macroforecast author.
heatmap – operational
Two-axis heatmap (feature × time / model / state).
Visualises importance across an additional dimension. Used for time-varying importance (rolling_recompute), per-state aggregation (group_aggregate over FRED-SD blocks), and pairwise interaction strength (shap_interaction). The us_state_choropleth figure is a specialised heatmap on the US state grid.
When to use
Time-varying importance, FRED-SD state choropleth, group-aggregate matrices.
References
macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Related options: auto, bar, boxplot, lineplot, scatter
Last reviewed 2026-05-05 by macroforecast author.
lineplot – operational
Line plot of importance over time / origin.
Tracks importance evolution across walk-forward origins. Pair with rolling_recompute to surface trends in which features matter as new data arrives.
When to use
Tracking importance evolution across walk-forward origins.
References
macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Related options: auto, bar, boxplot, heatmap, scatter
Last reviewed 2026-05-05 by macroforecast author.
scatter – operational
Scatter plot (e.g. SHAP value vs feature value).
PDP / ALE / SHAP dependence-plot family. Each point is a single observation; the x-axis is the feature value, the y-axis is the importance contribution. Reveals non-linearity in the model’s response.
When to use
PDP / ALE / SHAP dependence-plot family.
References
macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Related options: auto, bar, boxplot, heatmap, lineplot
Last reviewed 2026-05-05 by macroforecast author.