Description Usage Format Value Parameters Common Parameters See Also

This learner provides facilities for performing principal components analysis
(PCA) to reduce the dimensionality of a data set to a pre-specified value.
For further details, consult the documentation of `prcomp`

from the core
package `stats`

. This learner object is primarily intended for use with
other learners as part of a pre-processing pipeline.

1 |

`R6Class`

object.

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

for documentation on learners.

`n_comp`

A

`numeric`

value indicating the number of components to be produced as a result of the PCA dimensionality reduction. For convenience, this defaults to two (2) components.`center`

A

`logical`

value indicating whether the input data matrix should be centered before performing PCA. This defaults to`TRUE`

since that is the recommended practice. Consider consulting the documentation of`prcomp`

for details.`scale.`

A

`logical`

value indicating whether the input data matrix should be scaled (to unit variance) before performing PCA. Consider consulting the documentation of`prcomp`

for details.`...`

Other optional parameters to be passed to

`prcomp`

. Consider consulting the documentation of`prcomp`

for details.

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_optim`

,
`Lrnr_pkg_SuperLearner`

,
`Lrnr_randomForest`

,
`Lrnr_ranger`

, `Lrnr_rpart`

,
`Lrnr_rugarch`

, `Lrnr_sl`

,
`Lrnr_solnp_density`

,
`Lrnr_solnp`

,
`Lrnr_subset_covariates`

,
`Lrnr_svm`

, `Lrnr_tsDyn`

,
`Lrnr_xgboost`

, `Pipeline`

,
`Stack`

, `define_h2o_X`

,
`undocumented_learner`

jeremyrcoyle/sl3 documentation built on Oct. 13, 2018, 8:55 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.