This learner provides fitting procedures for models via non-negative linear
least squares regression, using nnls package's
nnls function.
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.
convex = FALSE: Normalize the coefficients to be a convex
combination.
...: Other parameters passed to nnls.
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_earth,
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_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)
covs <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs")
task <- sl3_Task$new(cpp_imputed, covariates = covs, outcome = "haz")
lrnr_nnls <- make_learner(Lrnr_nnls)
nnls_fit <- lrnr_nnls$train(task)
nnls_preds <- nnls_fit$predict()
# NNLS is commonly used as a metalearner in a super learner (i.e., Lrnr_sl)
lrnr_glm <- make_learner(Lrnr_glm)
lrnr_glmnet <- Lrnr_glmnet$new()
lrnr_mean <- Lrnr_mean$new()
learners <- c(lrnr_glm, lrnr_glmnet, lrnr_mean)
names(learners) <- c("glm", "lasso", "mean") # optional, renaming learners
simple_learner_stack <- make_learner(Stack, learners)
sl <- Lrnr_sl$new(learners = simple_learner_stack, metalearner = lrnr_nnls)
sl_fit <- sl$train(task)
sl_preds <- sl_fit$predict()