MachineLearningMulti | R Documentation |
Create an ensemble or comparison table of new MachineLearning and/or Regression models
MachineLearningMulti(
formula,
data = NULL,
subset = NULL,
weights = NULL,
evaluation.subset = NULL,
missing = "Exclude cases with missing data",
show.labels = FALSE,
seed = 12321,
models.args = NULL,
compare.only = FALSE,
optimal.ensemble = FALSE,
output = "Comparison"
)
formula |
A formula of the form |
data |
A |
subset |
An optional vector specifying a subset of observations to be
used in the fitting process, or, the name of a variable in |
weights |
An optional vector of sampling weights, or the
name of a variable in |
evaluation.subset |
An optional vector specifying a subset of observations to be used for evaluating the models. If not specified, models will only be compared on the training data. If models are not trained on the whole sample To evaluate on the whole sample, a subset must still be specified. |
missing |
How missing data is to be treated. Options:
|
show.labels |
Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")). |
seed |
The random number seed. |
models.args |
A |
compare.only |
Logical; whether to just produce a table comparing the models or additionally combine them to make a new ensemble model. |
optimal.ensemble |
Logical; whether to find the ensemble with the best accuracy or
r-squared, calculated on the |
output |
If |
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