Lrnr_earth: Earth: Multivariate Adaptive Regression Splines
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description Format Value Parameters References See Also Examples

This learner provides fitting procedures for building regression models thru the spline regression techniques described in \insertCitefriedman1991multivariate;textualsl3 and \insertCitefriedman1993fast;textualsl3, via earth and the function earth.

An R6Class object inheriting from Lrnr_base.

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.

\insertAllCited

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_glmnet, Lrnr_glm, 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)