pod.fit_all_robust_mean_models

fit_all_robust_mean_models(X, y, max_degree=10, n_folds=10)

Fits all polynomial models (and optionally Kriging) and returns them for caching.

Instead of fitting models, selecting the best, and throwing the rest away, this function evaluates all candidates via k-fold Cross Validation (CV) and then fits every model to the full dataset. This allows the application to instantly swap between different model structures without recalculating.

Parameters

Name	Type	Description	Default
X	np.ndarray	1D array or 2D matrix of input variable values.	required
y	np.ndarray	1D array of outcome values (e.g., signal response).	required
max_degree	int	The maximum polynomial degree to test. Defaults to 10.	10
n_folds	int	Number of folds for Cross Validation. Defaults to 10.	10

Returns

Name	Type	Description
	Tuple[Dict[Tuple[str, Any], Any], Dict[Tuple[str, Any], float], Tuple[str, Any]]	Tuple[Dict, Dict, Tuple]: - fitted_models: A dictionary mapping a key like ('Polynomial', 3) to the fully trained scikit-learn model. - cv_scores: A dictionary mapping the same keys to their Cross-Validation MSE scores. - cv_winner_key: The key of the model that achieved the lowest MSE.

Examples

import numpy as np
X = np.linspace(0, 10, 50)
y = 3 * X + np.random.normal(0, 1, 50)

models, scores, best_key = fit_all_robust_mean_models(X, y)

print(f"The best model was: {best_key}")

# Instantly retrieve the degree-4 polynomial without refitting
poly_4 = models[('Polynomial', 4)]