Lrnr_optim: Optimize Metalearner according to Loss Function using optim
In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Description Format Value Parameters Common Parameters See Also

This meta-learner provides fitting procedures for any pairing of loss function and metalearner function, subject to constraints. The optimization problem is solved by making use of optim, For further details, consult the documentation of optim.

R6Class object.

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

learner_function=metalearner_linear: A function(alpha, X) that takes a vector of covariates and a matrix of data and combines them into a vector of predictions. See metalearners for options.
loss_function=loss_squared_error: A function(pred, truth) that takes prediction and truth vectors and returns a loss vector. See loss_functions for options.
intercept=FALSE: If true, X includes an intercept term.
init_0=FALSE: If true, alpha is initialized to all 0's, useful for TMLE. Otherwise, it is initialized to equal weights summing to 1, useful for Super Learner.
...: Not currently used.

Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by Lrnr_base, and shared by all learners.

covariates: A character vector of covariates. The learner will use this to subset the covariates for any specified task
outcome_type: A variable_type object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified
...: All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating

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_nnls, 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