Lrnr_nnls: Non-negative Linear Least Squares
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description Format Value Parameters See Also Examples

This learner provides fitting procedures for models via non-negative linear least squares regression, using nnls package's nnls function.

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.

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()