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.
R6Class object.
Learner object with methods for training and prediction. See
Lrnr_base for documentation on learners.
n_compA 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.
centerA 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.
covariatesA character vector of covariates. The learner will use this to subset the covariates for any specified task
outcome_typeA 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_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_multivariate,
Lrnr_nnet,
Lrnr_nnls,
Lrnr_optim,
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
set.seed(37912)
# load example data
ncomp <- 3
data(cpp_imputed)
covars <- c(
"apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs",
"sexn"
)
outcome <- "haz"
# create sl3 task
task <- sl3_Task$new(cpp_imputed, covariates = covars, outcome = outcome)
# define learners
glm_fast <- Lrnr_glm_fast$new(intercept = FALSE)
pca_sl3 <- Lrnr_pca$new(n_comp = ncomp, center = TRUE, scale. = TRUE)
pcr_pipe_sl3 <- Pipeline$new(pca_sl3, glm_fast)
# create stacks + train and predict
pcr_pipe_sl3_fit <- pcr_pipe_sl3$train(task)
pcr_pred <- pcr_pipe_sl3_fit$predict()