Description Usage 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`

.

1 |

`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_bilstm`

, `Lrnr_condensier`

,
`Lrnr_cv`

, `Lrnr_dbarts`

,
`Lrnr_define_interactions`

,
`Lrnr_expSmooth`

,
`Lrnr_glm_fast`

, `Lrnr_glmnet`

,
`Lrnr_glm`

, `Lrnr_grf`

,
`Lrnr_h2o_grid`

, `Lrnr_hal9001`

,
`Lrnr_independent_binomial`

,
`Lrnr_lstm`

, `Lrnr_mean`

,
`Lrnr_nnls`

, `Lrnr_pca`

,
`Lrnr_pkg_SuperLearner`

,
`Lrnr_randomForest`

,
`Lrnr_ranger`

, `Lrnr_rpart`

,
`Lrnr_rugarch`

, `Lrnr_sl`

,
`Lrnr_solnp_density`

,
`Lrnr_solnp`

, `Lrnr_stratified`

,
`Lrnr_subset_covariates`

,
`Lrnr_svm`

, `Lrnr_tsDyn`

,
`Lrnr_xgboost`

, `Pipeline`

,
`Stack`

, `define_h2o_X`

,
`undocumented_learner`

jeremyrcoyle/sl3 documentation built on Oct. 16, 2018, 5:11 p.m.

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