A wrapper for glmnet::glmnet() for use in nadir::super_learner().
Arguments
- data
A dataframe to train a learner / learners on.
- formula
A regression formula to use inside this learner.
- weights
Observation weights; see
?lm- lambda
The multiplier parameter for the penalty; see
?glmnet::glmnet- ...
Any extra arguments that should be passed to the internal model for model fitting purposes.
Value
A prediction function that accepts newdata,
which returns predictions (a numeric vector of values, one for each row
of newdata).
Details
glmnet predictions will by default, if lambda is unspecified, return a matrix of predictions for varied lambda values, hence the need to explicitly handle the lambda argument in building glmnet learners.
Examples
lnr_glmnet(mtcars, mpg ~ hp + disp + am + wt, lambda = .5)(mtcars)
#> [1] 23.65983 22.88221 25.08887 20.77045 18.11832 20.17456 15.60465 22.29851
#> [9] 21.42236 19.69112 19.69112 16.04590 17.08273 16.93025 11.69132 10.85825
#> [17] 10.64547 26.27146 28.47886 27.41477 23.45078 18.63051 18.88972 14.78128
#> [25] 16.88327 27.07958 25.69827 26.94490 17.32470 21.23641 13.95742 23.20216