print.pre prints information about the generated prediction rule
ensemble to the command line
Usage
# S3 method for pre
print(x, penalty.par.val = "lambda.1se", digits = getOption("digits"), ...)
Value
Prints information about the fitted prediction rule ensemble.
Arguments
x
An object of class pre.
penalty.par.val
character or numeric. Value of the penalty parameter
\(\lambda\) to be employed for selecting the final ensemble. The default
"lambda.min" employs the \(\lambda\) value within 1 standard
error of the minimum cross-validated error. Alternatively,
"lambda.min" may be specified, to employ the \(\lambda\) value
with minimum cross-validated error, or a numeric value \(>0\) may be
specified, with higher values yielding a sparser ensemble. To evaluate the
trade-off between accuracy and sparsity of the final ensemble, inspect
pre_object$glmnet.fit and plot(pre_object$glmnet.fit).
digits
Number of decimal places to print
...
Further arguments to be passed to
coef.cv.glmnet.
Details
Note that the CV error is estimated with data that was also used
for learning rules and may be too optimistic. Use function cvpre to
obtain a more realistic estimate of future prediction error.