nnpiv.rkhs.ApproxRKHSIVCV

class nnpiv.rkhs.ApproxRKHSIVCV(kernel_approx='nystrom', n_components=10, kernel='rbf', gamma=2, degree=3, coef0=1, kernel_params=None, delta_scale='auto', delta_exp='auto', alpha_scales='auto', n_alphas=30, cv=6)[source]

Approximate RKHS IV estimator with cross-validation using kernel approximations.

Each feature approximation is fitted on its training fold and then used to transform the corresponding held-out fold.

Parameters

  • kernel_approx (str) – Kernel approximation method (‘nystrom’ or ‘rbfsampler’).

  • n_components (int or float) – Number of approximation components. Values in (0, 1] are sample fractions with a floor of 10; integer-like values greater than 1 are fixed component counts.

  • kernel (str or callable) – Kernel function or string identifier.

  • gamma (str or float) – Kernel coefficient passed to scikit-learn; for RBF, the kernel is exp(-gamma * ||x - x'||^2).

  • degree (int) – Degree for polynomial kernels.

  • coef0 (float) – Zero coefficient for polynomial kernels.

  • delta_scale (str or float) – Scale of the critical radius.

  • delta_exp (str or float) – Exponent of the critical radius.

  • alpha_scales (str or array-like) – Scale of the regularization parameter.

  • n_alphas (int) – Number of alpha scales to try.

  • cv (int) – Number of folds for cross-validation.

  • kernel_params (dict) – Additional parameters for the kernel.