# `split_kennard_stone` — Kennard Stone Splitter
_Group_: **Splitters** · _Binding_: `n4m.sklearn.KennardStoneSplitter` · _C ABI_: `n4m_split_kennard_stone_*`
## Description
Kennard-Stone train/test split.
Full binding docstring
```text
Kennard-Stone train/test split.
Picks the most diverse samples for the training set, in descending order
of pairwise Euclidean distance.
```
### Parameters
| Name | Type | Default |
|------|------|---------|
| `test_size` | `float` | `0.25` |
## Explanations
### Bibliographic source
_Standard spectroscopic operator — see the nirs4all preprocessing / augmentation handbook and the cited literature within the binding docstring._
### Mathematical principle
Kennard-Stone train/test split.
### Implementation
C ABI `n4m_split_kennard_stone_*` in libn4m (create / apply / destroy lifecycle), wrapped by `n4m.sklearn.KennardStoneSplitter`. The same numerical kernel backs every language binding.
### Usage
```python
from n4m.sklearn import KennardStoneSplitter
op = KennardStoneSplitter()
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
| Backend | Parity | 50×250 (ms) | 250×50 (ms) |
| C++ native · libn4m |
pls4all.cpp.blas+omp | ✓ ref | — | — |
| Python · external |
nirs4all | source | — | — |
:::
::::
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_See also_: [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)