GridSearchCv
Grid search with cross-validation — exhaustive hyperparameter search. / Recherche en grille avec validation croisée — recherche exhaustive d'hyperparamètres.
⚡ Rust-native
✓ sklearn parity
Quick start — Python
import seraplot as sp, json, numpy as np
X = np.random.randn(200, 5)
y = X[:, 0] * 2 + np.random.randn(200) * 0.3
payload = {"X_train": X.tolist(), "y_train": y.tolist(), "param_grid": {"alpha": [0.1, 1.0, 10.0]}}
res = json.loads(sp.ml_grid_search_cv(json.dumps(payload)))
print(res["best_params"], res["best_score"])
EN — Drop-in replacement:
FR — Remplacement direct : même API que sklearn, changez l'import.
sp.GridSearchCv has the same API as sklearn.FR — Remplacement direct : même API que sklearn, changez l'import.
API Reference
JSON function name
ml_grid_search_cv — aliases: grid_search_cv
Python class
sp.GridSearchCv(param_grid={}, n_splits=5, scoring=auto)
Constructor Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
param_grid | dict | {} | Hyperparameter grid to search. |
n_splits | int | 5 | Number of CV folds. |
scoring | str | auto | Scoring metric. |
Returns
JSON with best_params, best_score, cv_results.
Example
import seraplot as sp, json, numpy as np
X = np.random.randn(200, 5)
y = X[:, 0] * 2 + np.random.randn(200) * 0.3
payload = {"X_train": X.tolist(), "y_train": y.tolist(), "param_grid": {"alpha": [0.1, 1.0, 10.0]}}
res = json.loads(sp.ml_grid_search_cv(json.dumps(payload)))
print(res["best_params"], res["best_score"])
Référence API
Nom de fonction JSON
ml_grid_search_cv — alias : grid_search_cv
Classe Python
sp.GridSearchCv(param_grid={}, n_splits=5, scoring=auto)
Paramètres du constructeur
| Paramètre | Type | Défaut | Description |
|---|---|---|---|
param_grid | dict | {} | Grille d'hyperparamètres à explorer. |
n_splits | int | 5 | Nombre de plis CV. |
scoring | str | auto | Métrique de scoring. |
Retourne
JSON avec best_params, best_score, cv_results.
Exemple
import seraplot as sp, json, numpy as np
X = np.random.randn(200, 5)
y = X[:, 0] * 2 + np.random.randn(200) * 0.3
payload = {"X_train": X.tolist(), "y_train": y.tolist(), "param_grid": {"alpha": [0.1, 1.0, 10.0]}}
res = json.loads(sp.ml_grid_search_cv(json.dumps(payload)))
print(res["best_params"], res["best_score"])