moment_stack — n4m.ensemble.moment_stack¶
Namespace: n4m.ensemble · Fully-qualified: n4m.ensemble.moment_stack · Catalog id: models.ensembles.moment_stack
API surface¶
C ABI: no standalone exported symbol is declared for this method.
Python (verified public re-export): from n4m.ensemble import moment_stack
Signature: moment_stack(X, y, **kwargs)
R: no current source-verified entry point was found for this catalog method.
MATLAB / Octave: no current source-verified entry point was found for this catalog method.
Parameters¶
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Explanations¶
Bibliographic source¶
No canonical paper defines this n4m moment-stack product route; its implementation is a documented linear stack over moment-compatible candidate predictions.
Mathematical principle¶
Build a candidate prediction matrix from the declared moment-compatible routes and fit a linear meta-level combination using out-of-fold predictions, so the stack learns weights without reusing a candidate’s own fit rows.
Appropriate uses¶
Combining a fixed, auditable set of linear/moment-compatible spectral predictors when complementary errors are expected.
Limits and validation¶
The stack requires a valid OOF plan and can overfit when many highly correlated candidates are included. It is a product composition rather than a claim of a new statistical estimator; assess it in an outer validation loop.
Implementation¶
n4m.ensemble.moment_stack and MomentStack; no standalone C ABI symbol.
Sources and provenance¶
https://github.com/GBeurier/nirs4all-methods/blob/main/bindings/python/src/n4m/ensemble.py; https://github.com/GBeurier/nirs4all-methods/blob/main/bindings/python/src/n4m/_impl/moment_facade.py
Catalog note¶
Python-backed train-only OOF linear stack over native moment-compatible Ridge, PLS sweep, PCR, continuum, ECR and CPPLS heads. Base predictions are fit on training folds only; the final reusable estimator fits the same base heads on all rows and applies a small Ridge meta-model over base predictions. This is a moment-model composition, not a nonlinear feature lift and not a fused CUDA grinder.
Timing benchmark: benchmarks/cross_binding/bench_moment_stack_timing.py
See also: methods index.