A wrapper for rpart::rpart() 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- ...
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
A single regression tree is a classic member of super learner libraries:
it is fast, handles interactions and nonlinearities automatically, and its
predictions are piecewise-constant, complementing the smooth learners in
the library. Complexity may be controlled through rpart.control
arguments passed via ... (e.g., cp, minsplit,
maxdepth).
Examples
lnr_rpart(mtcars, mpg ~ hp + disp + wt)(mtcars)
#> [1] 20.75455 20.75455 29.02857 20.75455 15.10000 20.75455 15.10000 20.75455
#> [9] 20.75455 20.75455 20.75455 15.10000 15.10000 15.10000 15.10000 15.10000
#> [17] 15.10000 29.02857 29.02857 29.02857 20.75455 15.10000 15.10000 15.10000
#> [25] 15.10000 29.02857 29.02857 29.02857 15.10000 20.75455 15.10000 20.75455
lnr_rpart(mtcars, mpg ~ ., cp = 0.05)(mtcars)
#> [1] 18.26429 18.26429 26.66364 18.26429 18.26429 18.26429 13.41429 26.66364
#> [9] 26.66364 18.26429 18.26429 18.26429 18.26429 18.26429 13.41429 13.41429
#> [17] 13.41429 26.66364 26.66364 26.66364 26.66364 18.26429 18.26429 13.41429
#> [25] 18.26429 26.66364 26.66364 26.66364 13.41429 18.26429 13.41429 26.66364