pod.fit_robust_mean_model

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

Fits regression models (Polynomials and Kriging) and selects the optimal one.

This function performs k-fold Cross Validation (CV) to find the model type (Polynomial or Kriging) and parameters (e.g., degree) that minimize the Mean Squared Error (MSE), balancing bias and variance.

Parameters

Name	Type	Description	Default
X	np.ndarray	1D array of input variable values (e.g., flaw size).	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
Any	Any	A fitted scikit-learn model with the following added attributes:
	Any	- model_type_ (str): Either ‘Polynomial’ or ‘Kriging’.
	Any	- model_params_ (Any): The selected integer degree or the fitted kernel.
	Any	- cv_scores_ (dict): The cross-validation MSE scores for all tested models.