# `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_feature_selection_shaving_select`. R roxygen note (`methods_extra.R::shaving_select`): > Shaving selector. > @param n_components Integer. Number of latent components. > @param n_steps Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param min_features Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param shave_fraction Method-specific parameter. See the underlying `*_fit()` function for the exact semantics. > @param X Numeric matrix of predictors (rows = samples, cols = features). > @param Y Numeric matrix or vector of responses, with one row per sample. > @export ### 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_feature_selection_shaving_select(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} R · pls4all (raw fn) :sync: r-raw :class-label: lang-r ```r library(pls4all) res <- shaving_select(X, Y, n_components, n_steps = 10L, min_features = 5L, shave_fraction = 0.1) yhat <- pls4all_predict(res, X_test) ``` ::: :::{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  ·  ⇄ cross-check = documented by-design selector/RNG/model, noncanonical API/facade convention, or secondary oracle  ·  ✗ 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
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.001.0 s
Python · pls4all
pls4all.python✓ J 1.00440.9 ms
pls4all.sklearn⇄ J 0.502.32 ms🏆
R · pls4all
pls4all.R⇄ J 0.505.59 ms
pls4all.R.formula⇄ J 0.507.94 ms
pls4all.R.mdatools⇄ J 0.506.31 ms
pls4all.R.pls⇄ J 0.506.29 ms
R · external
📐ref.r_plsvarselsource38.2 ms
::: :::{tab-item} 3 threads :sync: threads-3
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.00424.1 ms
Python · pls4all
pls4all.python✓ J 1.00445.2 ms
pls4all.sklearn⇄ J 0.502.24 ms🏆
R · pls4all
pls4all.R⇄ J 0.505.24 ms
pls4all.R.formula⇄ J 0.505.90 ms
pls4all.R.mdatools⇄ J 0.506.06 ms
pls4all.R.pls⇄ J 0.505.99 ms
R · external
📐ref.r_plsvarselsource94.0 ms
::: :::{tab-item} 10 threads :sync: threads-10
BackendParity200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp✓ J 1.00454.0 ms
Python · pls4all
pls4all.python✓ J 1.00443.2 ms
pls4all.sklearn⇄ J 0.502.26 ms🏆
R · pls4all
pls4all.R⇄ J 0.505.07 ms
pls4all.R.formula⇄ J 0.505.85 ms
pls4all.R.mdatools⇄ J 0.505.96 ms
pls4all.R.pls⇄ J 0.505.97 ms
R · external
📐ref.r_plsvarselsource35.1 ms
::: :::: --- _See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)