feature_correlation

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Axis feature_correlation on sub-layer L3_5_C_feature_correlation (layer l3_5).

Sub-layer

L3_5_C_feature_correlation

Axis metadata

  • Default: 'cross_block'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 5 option(s)

  • Future: 0 option(s)

Options

cross_block – operational

Correlations across blocks (e.g. PCA factors vs MARX features).

L3.5.C feature correlation view cross_block.

This option configures the feature_correlation axis on the L3_5_C_feature_correlation sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_C_feature_correlation/ alongside the other selected views.

When to use

Detecting block-level redundancy before L4; informs whether to drop a block.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: within_block, with_target, multi, none

Last reviewed 2026-05-05 by macroforecast author.

multi – operational

Run every feature-correlation view together.

L3.5.C feature correlation view multi.

This option configures the feature_correlation axis on the L3_5_C_feature_correlation sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_C_feature_correlation/ alongside the other selected views.

When to use

Default rich correlation audit.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: within_block, cross_block, with_target, none

Last reviewed 2026-05-05 by macroforecast author.

none – operational

Skip feature correlation diagnostic entirely.

L3.5.C feature correlation view none.

This option configures the feature_correlation axis on the L3_5_C_feature_correlation sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_C_feature_correlation/ alongside the other selected views.

When to use

Memory-constrained sweeps with very wide feature panels (n_features > 5000).

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: within_block, cross_block, with_target, multi

Last reviewed 2026-05-05 by macroforecast author.

with_target – operational

Correlations of every feature with the target.

L3.5.C feature correlation view with_target.

This option configures the feature_correlation axis on the L3_5_C_feature_correlation sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_C_feature_correlation/ alongside the other selected views.

When to use

Spotting top candidate predictors; pairs naturally with the L7 cumulative_r2_contribution op for downstream interpretation.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: within_block, cross_block, multi, none

Last reviewed 2026-05-05 by macroforecast author.

within_block – operational

Correlations within a feature block (e.g. lags of one series, PCA factors).

L3.5.C feature correlation view within_block.

This option configures the feature_correlation axis on the L3_5_C_feature_correlation sub-layer of L3.5; output is emitted under manifest.diagnostics/l3_5/L3_5_C_feature_correlation/ alongside the other selected views.

When to use

Detecting redundancy within a block – high within-block correlations suggest a smaller block dimension would suffice.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: cross_block, with_target, multi, none

Last reviewed 2026-05-05 by macroforecast author.