Construct a learner — learner

Constructs a learner class object for fitting multivariate adaptive regression splines with earth::earth.

learner_mars(
  formula,
  info = "earth::earth",
  degree = 1,
  nprune = NULL,
  glm = NULL,
  learner.args = NULL,
  ...
)

Arguments

formula: (formula) Formula specifying response and design matrix.
info: (character) Optional information to describe the instantiated learner object.
degree: Maximum degree of interaction (Friedman's $mi$). Default is 1, meaning build an additive model (i.e., no interaction terms).
nprune: Maximum number of terms (including intercept) in the pruned model. Default is NULL, meaning all terms created by the forward pass (but typically not all terms will remain after pruning). Use this to enforce an upper bound on the model size (that is less than nk), or to reduce exhaustive search time with pmethod="exhaustive".

The following arguments are for cross validation.
glm: NULL (default) or a list of arguments to pass on to glm. See the documentation of glm for a description of these arguments See “Generalized linear models” in the vignette. Example:
earth(survived~., data=etitanic, degree=2, glm=list(family=binomial))

The following arguments are for the forward pass.
learner.args: (list) Additional arguments to learner$new().
...: Additional arguments to earth::earth.

Value

learner object.

Examples

# poisson regression
n <- 5e2
x <- rnorm(n)
w <- 50 + rexp(n, rate = 1 / 5)
y <- rpois(n, exp(2 + 0.5 * x + log(w)) * rgamma(n, 1 / 2, 1 / 2))
d0 <- data.frame(y, x, w)

lr <- learner_mars(y ~ x + offset(log(w)), degree = 2,
  glm = list(family = poisson())
)
lr$estimate(d0)
lr$predict(data.frame(x = 0, w = c(1, 2)))
#> [1]  5.659458 11.318916