Lrnr_multivariate: Multivariate Learner

In jeremyrcoyle/sl3: Pipelines for Machine Learning and Super Learning

Multivariate Learner

Description

This learner applies a univariate outcome learner across a vector of outcome variables, effectively transforming it into a multivariate outcome learner

Format

R6Class object.

Value

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

Parameters

learner

The learner to wrap.

Common Parameters

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

See Also

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_earth, 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_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

library(data.table)

# simulate data
set.seed(123)
n <- 1000
p <- 5
pY <- 3
W <- matrix(rnorm(n * p), nrow = n)
colnames(W) <- sprintf("W%d", seq_len(p))
Y <- matrix(rnorm(n * pY, 0, 0.2) + W[, 1], nrow = n)
colnames(Y) <- sprintf("Y%d", seq_len(pY))
data <- data.table(W, Y)
covariates <- grep("W", names(data), value = TRUE)
outcomes <- grep("Y", names(data), value = TRUE)

# make sl3 task
task <- sl3_Task$new(data.table::copy(data),
  covariates = covariates,
  outcome = outcomes
)

# train multivariate learner and make predictions
mv_learner <- make_learner(Lrnr_multivariate, make_learner(Lrnr_glm_fast))
mv_fit <- mv_learner$train(task)
mv_pred <- mv_fit$predict(task)
mv_pred <- unpack_predictions(mv_pred)

jeremyrcoyle/sl3 documentation built on Nov. 18, 2024, 4:21 p.m.