Description Usage Format Value Parameters Common Parameters See Also

This learner implements Bayesian Additive Regression Trees, using the
`bartMachine`

package.

1 |

`R6Class`

object.

Learner object with methods for training and prediction. See
`Lrnr_base`

for documentation on learners.

`Y`

Outcome variable.

`X`

Covariate dataframe.

`newX`

Optional dataframe to predict the outcome.

`obsWeights`

Optional observation-level weights (supported but not tested).

`id`

Optional id to group observations from the same unit (not used currently).

`family`

"gaussian" for regression, "binomial" for binary classification.

`num_trees`

The number of trees to be grown in the sum-of-trees model.

`num_burn_in`

Number of MCMC samples to be discarded as "burn-in".

`num_iterations_after_burn_in`

Number of MCMC samples to draw from the posterior distribution of f(x).

`alpha`

Base hyperparameter in tree prior for whether a node is nonterminal or not.

`beta`

Power hyperparameter in tree prior for whether a node is nonterminal or not.

`k`

For regression, k determines the prior probability that E(Y|X) is contained in the interval (y_min, y_max), based on a normal distribution. For example, when k=2, the prior probability is 95%. For classification, k determines the prior probability that E(Y|X) is between (-3,3). Note that a larger value of k results in more shrinkage and a more conservative fit.

`q`

Quantile of the prior on the error variance at which the data-based estimate is placed. Note that the larger the value of q, the more aggressive the fit as you are placing more prior weight on values lower than the data-based estimate. Not used for classification.

`nu`

Degrees of freedom for the inverse chi^2 prior. Not used for classification.

`verbose`

Prints information about progress of the algorithm to the screen.

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_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_optim`

,
`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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