This learner provides fitting procedures for building regression models thru
the spline regression techniques described in
Friedman (1991)
and
Friedman (1993)
, via earth and the function
earth.
A learner object inheriting from Lrnr_base with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of Lrnr_base.
degree: A numeric specifying the maximum degree of
interactions to be used in the model. This defaults to 2, specifying
up through one-way interaction terms. Note that this differs from the
default of earth.
penalty: Generalized Cross Validation (GCV) penalty per knot.
Defaults to 3 as per the recommendation for degree > 1 in the
documentation of earth. Special values (for use
by knowledgeable users): The value 0 penalizes only terms, not knots.
The value -1 translates to no penalty.
pmethod: Pruning method, defaulting to "backward". Other
options include "none", "exhaustive", "forward",
"seqrep", "cv".
nfold: Number of cross-validation folds. The default is 0, for no
cross-validation.
ncross: Only applies if nfold > 1, indicating the number
of cross-validation rounds. Each cross-validation has nfold
folds. Defaults to 1.
minspan: Minimum number of observations between knots.
endspan: Minimum number of observations before the first and
after the final knot.
...: Other parameters passed to earth. See
its documentation for details.
Friedman JH (1991).
“Multivariate adaptive regression splines.”
The Annals of Statistics, 1--67.
Friedman JH (1993).
“Fast MARS.”
Stanford University.
https://statistics.stanford.edu/sites/g/files/sbiybj6031/f/LCS%20110.pdf.
Other Learners:
Custom_chain,
Lrnr_HarmonicReg,
Lrnr_arima,
Lrnr_bartMachine,
Lrnr_base,
Lrnr_bayesglm,
Lrnr_bilstm,
Lrnr_caret,
Lrnr_cv_selector,
Lrnr_cv,
Lrnr_dbarts,
Lrnr_define_interactions,
Lrnr_density_discretize,
Lrnr_density_hse,
Lrnr_density_semiparametric,
Lrnr_expSmooth,
Lrnr_gam,
Lrnr_ga,
Lrnr_gbm,
Lrnr_glm_fast,
Lrnr_glm_semiparametric,
Lrnr_glmnet,
Lrnr_glmtree,
Lrnr_glm,
Lrnr_grfcate,
Lrnr_grf,
Lrnr_gru_keras,
Lrnr_gts,
Lrnr_h2o_grid,
Lrnr_hal9001,
Lrnr_haldensify,
Lrnr_hts,
Lrnr_independent_binomial,
Lrnr_lightgbm,
Lrnr_lstm_keras,
Lrnr_mean,
Lrnr_multiple_ts,
Lrnr_multivariate,
Lrnr_nnet,
Lrnr_nnls,
Lrnr_optim,
Lrnr_pca,
Lrnr_pkg_SuperLearner,
Lrnr_polspline,
Lrnr_pooled_hazards,
Lrnr_randomForest,
Lrnr_ranger,
Lrnr_revere_task,
Lrnr_rpart,
Lrnr_rugarch,
Lrnr_screener_augment,
Lrnr_screener_coefs,
Lrnr_screener_correlation,
Lrnr_screener_importance,
Lrnr_sl,
Lrnr_solnp_density,
Lrnr_solnp,
Lrnr_stratified,
Lrnr_subset_covariates,
Lrnr_svm,
Lrnr_tsDyn,
Lrnr_ts_weights,
Lrnr_xgboost,
Pipeline,
Stack,
define_h2o_X(),
undocumented_learner
data(cpp_imputed)
covars <- c(
"apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs", "sexn"
)
outcome <- "haz"
task <- sl3_Task$new(cpp_imputed,
covariates = covars,
outcome = outcome
)
# fit and predict from a MARS model
earth_lrnr <- make_learner(Lrnr_earth)
earth_fit <- earth_lrnr$train(task)
earth_preds <- earth_fit$predict(task)