ktd_cv {ktweedie}

Cross validation for tuning the regularization coefficient in the kernel Tweedie model

Description

ktd_cv() performs cross-validation to determine the optimal regularization coefficient of the ktweedie model.

Usage

ktd_cv(x, y, kern, lambda, nfolds = 5, rho = 1.5, loss = "LL", ...)

Arguments

x

Covariate matrix.

y

Outcome vector (e.g. insurance cost).

kern

Choice of kernel. See dots for details on supported kernel functions.

lambda

A vector of candidate regularization coefficients used in cross-validation.

nfolds

Number of folds in cross-validation. Default is 5.

rho

The power parameter of the Tweedie model. Default is 1.5 and can take any real value between 1 and 2.

loss

Criterion used in cross-validation. "LL" for log likelihood, "RMSE" for root mean squared error, "MAD" for mean absolute difference. Default is "LL".

...

Optional arguments to be passed to ktd_estimate().

Details

ktd_cv() is a built-in wrapper for cross-validation for the choice of regularization coefficient.

Value

A list of two items.

1. LL or RMSE or MAD: a vector of validation error based on the user-specified loss, named by the corresponding lambda values;

2. Best_lambda: the lambda value in the pair that generates the best loss;

See Also

ktd_cv2d, ktd_estimate, ktd_predict

Examples

# Provide a sequence of candidate values to the argument lambda.
# ktd_cv() will perform cross-validation to determine which is the best.
( cv1d <- ktd_cv(x = dat$x, y = dat$y,
                 kern = rbfdot(sigma = 1e-8),
                 lambda = 10^(-8:-1),
                 nfolds = 5) )