Fit a bagging ridge regression model as implemented in scikit-learn (python)

Fit a bagging ridge regression model as implemented in scikit-learn (python)

fit_bagging_ridge(
  formula,
  data,
  alpha = 1,
  solver = "auto",
  bagging_number = 200L,
  n_jobs = 1,
  p_method = c("wilcox", "t"),
  ...
)

Arguments

formula	An object of class formula with a symbolic description of the model to be fitted.
data	A data.frame containing the variables in the model.
alpha	Positive float indicating the regularization strength.
solver	Solver to use in the computational routines. Options include ‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’.
bagging_number	The number of ridge regression model in the bagging.
n_jobs	The number of cores used to fit the model.
p_method	The test used to calculate p-values. Options are 't' for t.test, and 'wilcox' for wilcox.test
...	Other parameters for the model fitting function.

Value

A list with two data frames: gof contains goodness of fit measures of the fit and coefs contains the fitted coefficients.