View source: R/predictions.with.superlearner.r
This function trains SuperLearner on the training set and makes predictions on the review set. Then, predictions are divided by quantiles into four colour-coded groups. The groups are green, yellow, orange, and red. They respectively include ranges from the 0
1 2 3 4 5 6 7 | predictions.with.superlearner(prepped_sample, models = c("SL.glmnet",
"SL.glm", "SL.randomForest", "SL.xgboost", "SL.gam"),
save_breaks = FALSE, save_all_predictions = FALSE,
save_superlearner = FALSE, sample = TRUE,
gridsearch_parallel = FALSE, n_cores = NULL, verbose = TRUE,
log = FALSE, boot = FALSE, write_to_disk = FALSE,
clean_start = FALSE)
|
prepped_sample |
Data as prepared by prep.data.for.superlearner as list. No default. |
models |
Models to use in ensemble algorithm. Default: SL.mean and SL.glmnet. |
save_breaks |
Logical. If TRUE, save optimal breaks to results. Defaults to FALSE. |
save_all_predictions |
Logical. If TRUE all predictions are saved in pred_data. Defaults to FALSE. |
save_superlearner |
Logical. If TRUE the SuperLearner object is saved to disk and returned to results. Defaults to FALSE. |
sample |
Logical. If TRUE the grid search will only search a random sample of possible cutpoint combinations, not all. Defaults to TRUE. |
gridsearch_parallel |
Logical. If TRUE the gridsearch is performed in parallel. Defaults to FALSE. |
n_cores |
Integer. The number of cores to run any parallel computing on. Defaults to NULL. |
verbose |
Logical. If TRUE information to help gauge progress is printed. Defaults to TRUE. |
log |
Logical. If TRUE progress is logged in logfile. Defaults to FALSE. |
boot |
Logical. Affects only what is printed to logfile. If TRUE prepped_sample is assumed to be a bootstrap sample. Defaults to FALSE. |
write_to_disk |
Logical. If TRUE the prediction data is saved as RDS to disk. Defaults to FALSE. |
clean_start |
Logical. If TRUE the predictions directory and all files in it are removed before saving new stuff there. Defaults to FALSE. |
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