# `filter_leverage` — High Leverage Filter _Group_: **Sample / feature filters** · _Binding_: `n4m.sklearn.HighLeverageFilter` · _C ABI_: `n4m_filter_leverage_*` ## Description Hat-matrix or PCA score-space leverage filter. ### Parameters | Name | Type | Default | |------|------|---------| | `method` | `str | int` | `'hat'` | | `threshold_multiplier` | `float` | `2.0` | | `absolute_threshold` | `float | None` | `None` | | `n_components` | `int` | `0` | | `center` | `bool` | `True` | ## Explanations ### Bibliographic source _Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring._ ### Mathematical principle Hat-matrix or PCA score-space leverage filter. ### Implementation C ABI `n4m_filter_leverage_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.HighLeverageFilter`. The same numerical kernel backs every language binding. ### Usage ```python from n4m.sklearn import HighLeverageFilter op = HighLeverageFilter() X_transformed = op.fit_transform(X) ``` ### Benchmarks Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). Only backends that implement this method are listed; libraries without the method are omitted. **Verdict**  ·  ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance  ·  ✓ bind = pls4all binding agrees with the C++ baseline  ·  ✗ divergent  ·  ⚠ error  ·  — not run. The fastest backend per column is marked 🏆. **Reference gate**: strict — numeric equivalence (`rmse_rel_tol ≤ 1e-12`). ::::{tab-set} :class: parity-tabs :::{tab-item} 1 thread :sync: threads-1
BackendParity50×250 (ms)250×50 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ ref
Python · external
nirs4allsource
::: :::: --- _See also_: [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)