| update_ade_meta {tsensembler} | R Documentation |
Updating the metalearning layer of an ADE model
Description
The update_ade_meta function uses new information to
update the meta models of an ADE-class ensemble. As input
it receives a ADE-class model object class and a new dataset
for updating the weights of the base models in the ensemble.
This new data should have the same structure as the one used to build the
ensemble. Updating the base models of the ensemble is done using the update_base_models
function.
Usage
update_ade_meta(object, newdata, num_cores = 1)
## S4 method for signature 'ADE'
update_ade_meta(object, newdata, num_cores = 1)
Arguments
object |
a |
newdata |
data used to update the meta models. This should be
the data used to initially train the meta-models (training set), together
with new observations (for example, validation set). Each meta model
is retrained using |
num_cores |
A numeric value to specify the number of cores used to train base and meta models. num_cores = 1 leads to sequential training of models. num_cores > 1 splits the training of the base models across num_cores cores. |
See Also
ADE-class for building an ADE model;
update_weights for updating the weights of the ensemble (without
retraining the models); and update_base_models for updating the
base models of an ensemble.
Other updating models:
update_ade(),
update_weights()
Examples
## Not run:
specs <- model_specs(
learner = c("bm_svr", "bm_glm", "bm_mars"),
learner_pars = NULL
)
data("water_consumption")
dataset <- embed_timeseries(water_consumption, 5)
train <- dataset[1:1000, ]
validation <- dataset[1001:1200, ]
test <- dataset[1201:1500, ]
model <- ADE(target ~., train, specs)
preds_val <- predict(model, validation)
model <- update_ade_meta(model, rbind.data.frame(train, validation))
preds_test <- predict(model, test)
## End(Not run)