# `shaving_select` — Shaving (recursive elimination)
_Group_: **Variable selector** · _Registry tolerance_: `1e-06`
## Description
Shaving iterative variable trimming
From the `pls4all.sklearn.ShavingSelector` docstring:
> Iterative SR-shaving variable elimination.
> **Registry note** — R `plsVarSel::shaving(method='SR')` iterative selectivity-ratio trimming — CV-error-minimising survivor set. Default `_shaving_select_pls4all` path mirrors the same R call with seed=11, giving bit-exact mask parity. The C++ step-count trajectory kernel is opt-in via `legacy=True`.
### Parameters
| Name | Type | Default | Notes |
|------|------|---------|-------|
| `n_components` | `int` | `2` | Number of latent components extracted (k). |
| `n_steps` | `int` | `10` | Number of elimination passes performed. |
| `min_features` | `int | None` | `None` | Minimum number of variables the selector is allowed to keep (defaults to `n_components`). |
| `shave_fraction` | `float` | `0.2` | Fraction of the worst-ranked variables removed at each shaving step. |
| `n_folds` | `int` | `3` | Number of cross-validation folds used inside the selector. |
## Explanations
### Bibliographic source
Mehmood, T., Liland, K. H., Snipen, L. & Sæbø, S. (2012). *A review of variable selection methods in partial least squares regression*. Chemometrics and Intelligent Laboratory Systems 118, 62–69 (§3.2 Shaving).
### Mathematical principle
Shaving recursively eliminates a fraction $\rho \in (0, 1)$ of the lowest-scoring features at each step, refits PLS, and tracks the CV-RMSE of the shrinking subset. The subset with the lowest recorded CV-RMSE across the whole shaving trajectory is returned.
Compared to backward variable elimination (BVE — see next), shaving removes a **batch** of features per step instead of one, which is faster ($O(\log p)$ steps vs $O(p)$) but more aggressive — a single bad shave removes many useful features irrecoverably. Recommended $\rho \le 0.2$ to keep shave granularity reasonable.
### Implementation
`n4m_shaving_select`.
### Usage
Every pls4all binding tab dispatches into the same C kernel; the external libraries listed at the bottom of the page are the parity references registered in `benchmarks.parity_timing.registry`. Switch tabs to read the same fit in your language. The R package now ships drop-in-compatible facades for the CRAN `pls` package (`plsr`, `pcr`, `mvr`) and for the `mdatools::pls(x, y, ...)` matrix idiom — those tabs appear only on the methods that have a meaningful equivalence.
**pls4all bindings**
::::{tab-set}
:class: pls4all-bindings
:::{tab-item} C ABI · libn4m
:sync: c
:class-label: lang-c
```c
/* C ABI — libn4m */
n4m_context_t* ctx = n4m_context_create();
n4m_config_t* cfg = n4m_config_create();
n4m_method_result_t* res = NULL;
n4m_shaving_select_fit(ctx, cfg, &x_view, &y_view, /* hyperparams */, &res);
/* … read coefficients / mask / scores via */
/* n4m_method_result_get_double_matrix / vector / scalar … */
n4m_method_result_destroy(res);
n4m_config_destroy(cfg);
n4m_context_destroy(ctx);
```
:::
:::{tab-item} Python · pls4all (raw)
:sync: python-raw
:class-label: lang-python
```python
import pls4all
from pls4all._methods import shaving_select_fit
with pls4all.Context() as ctx, pls4all.Config() as cfg:
res = shaving_select_fit(ctx, cfg, X, y, n_components=3)
# then: res.matrix("predictions"), res.matrix("coefficients"),
# res.vector("mask"), res.scalar("intercept"), …
```
:::
:::{tab-item} Python · pls4all.sklearn
:sync: python-sklearn
:class-label: lang-python
```python
from pls4all.sklearn import ShavingSelector
mdl = ShavingSelector(n_components=2, n_steps=10, min_features=None, shave_fraction=0.2, n_folds=3)
mdl.fit(X, y)
y_hat = mdl.predict(X_test)
```
:::
:::{tab-item} R · pls4all_method()
:sync: r-dispatcher
:class-label: lang-r
```r
library(pls4all)
# Unified low-level dispatcher (May 2026 R cleanup):
res <- pls4all_method("shaving_select", X, y,
n_components = 3L, params = list(n_steps = 12L, min_features = 3L, shave_fraction = 0.2))
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.
```
:::
:::{tab-item} MATLAB · pls4all (MEX)
:sync: matlab-mex
:class-label: lang-matlab
```matlab
res = pls4all.shaving_select(X, y, 3);
% see header of bindings/matlab/+pls4all/shaving_select.m for full
% parameter surface:
% res = shaving_select(X, Y, n_components, n_steps, min_features, shave_fraction)
yhat = predict(res, Xtest);
```
:::
:::{tab-item} MATLAB · pls4all (classdef)
:sync: matlab-classdef
:class-label: lang-matlab
_No idiomatic classdef wrapper — invoke `pls4all.fit("shaving_select", X, y, …)` directly from the unified MEX factory._
:::
::::
**Registry parity references** 📐
:::{card}
:class-card: external-refs
- 📐 **`ref.r_plsvarsel`** (R · r) — `plsVarSel` 0.10.0 · strict (rmse_rel ≤ 1e-06) — R `plsVarSel::shaving(method='SR')` — iterative SR-shaving of low-importance features. Uses the same `set.seed(11)` as the pls4all wrapper so the CV fold assignments coincide.
:::
### 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-06`).
Rows tagged with **📐** are the canonical parity references for this method (declared in [`parity_timing.registry`](../benchmarks/methodology.md)). C++ and external rows show reference parity; pls4all language bindings show binding parity against the C++ backend. Hover the icon for role and tolerance band.
::::{tab-set}
:class: parity-tabs
:::{tab-item} 1 thread
:sync: threads-1
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ≈ | 484.9 ms | 3.3 s | 4.0 s | 8.6 s | 558.1 ms | 502.9 ms | 3.6 s | 7.6 s | 28.1 s | 4.7 s | 26.1 s | 168.1 s🏆 | 11.6 s | 169.6 s |
pls4all.cpp.blas+omp | ≈ | 494.3 ms | 3.1 s🏆 | 3.6 s🏆 | 8.4 s | 569.1 ms | 489.9 ms | 3.6 s | 7.9 s | 27.5 s | 4.9 s | 26.3 s | 171.4 s | 11.1 s🏆 | 170.2 s |
pls4all.cpp.omp | ≈ | 491.9 ms | 3.2 s | 3.7 s | 8.3 s🏆 | 569.9 ms | 486.5 ms | 3.4 s🏆 | 6.8 s🏆 | 28.6 s | 4.2 s🏆 | 25.5 s🏆 | 183.2 s | 11.7 s | 160.6 s🏆 |
pls4all.cpp.ref | ≈ | 509.7 ms | 3.2 s | 4.2 s | 8.6 s | 563.0 ms | 484.5 ms | 3.5 s | 7.0 s | 25.5 s🏆 | 4.6 s | 25.7 s | 188.4 s | 11.7 s | 174.8 s |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | 507.0 ms | — | — | — | 549.0 ms | 489.3 ms | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✗ +1e+00 | 3.84 ms | — | — | — | 3.90 ms | 5.24 ms | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | 15.2 ms | — | — | — | 7.71 ms | 13.0 ms | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | 22.9 ms | — | — | — | 8.84 ms | 12.8 ms | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | 24.2 ms | — | — | — | 8.83 ms | 13.2 ms | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | 22.5 ms | — | — | — | 10.9 ms | 12.6 ms | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | 4.85 ms | — | — | — | 3.40 ms | 6.83 ms | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | 5.70 ms | — | — | — | 3.82 ms | 6.61 ms | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | 59.0 ms🏆 | — | — | — | 46.7 ms🏆 | 43.8 ms🏆 | — | — | — | — | — | — | — | — |
:::
:::{tab-item} 3 threads
:sync: threads-3
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ✓ ref | — | — | — | — | 485.6 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.blas+omp | ✓ ref | — | — | — | — | 496.4 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.omp | ✓ ref | — | — | — | — | 478.1 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.ref | ✓ ref | — | — | — | — | 488.2 ms | — | — | — | — | — | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | — | — | — | — | 490.7 ms | — | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✗ +1e+00 | — | — | — | — | 2.45 ms | — | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | — | — | — | — | 7.12 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | — | — | — | — | 8.17 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | — | — | — | — | 7.92 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | — | — | — | — | 8.03 ms | — | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | — | — | — | — | 3.14 ms | — | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | — | — | — | — | 4.07 ms | — | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | — | — | — | — | 38.7 ms🏆 | — | — | — | — | — | — | — | — | — |
:::
:::{tab-item} 10 threads
:sync: threads-10
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ✓ ref | — | — | — | — | 409.4 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.blas+omp | ✓ ref | — | — | — | — | 399.9 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.omp | ✓ ref | — | — | — | — | 408.8 ms | — | — | — | — | — | — | — | — | — |
pls4all.cpp.ref | ✓ ref | — | — | — | — | 409.5 ms | — | — | — | — | — | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | — | — | — | — | 410.7 ms | — | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✗ +1e+00 | — | — | — | — | 2.26 ms | — | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | — | — | — | — | 5.76 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | — | — | — | — | 6.93 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | — | — | — | — | 6.75 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | — | — | — | — | 6.84 ms | — | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | — | — | — | — | 2.99 ms | — | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | — | — | — | — | 3.50 ms | — | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | — | — | — | — | 32.8 ms🏆 | — | — | — | — | — | — | — | — | — |
:::
::::
---
_See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)