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

Lrnr_earth

R Documentation

Earth: Multivariate Adaptive Regression Splines

Description

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.

Format

An R6Class object inheriting from Lrnr_base.

Value

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.

Parameters

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.

References

\insertAllCited

Other Learners: Custom_chain, Lrnr_HarmonicReg, Lrnr_arima, Lrnr_bartMachine, Lrnr_base, Lrnr_bayesglm, 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

Examples

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)

tlverse/sl3 documentation built on Nov. 18, 2024, 12:46 a.m.