Random Forest
Usage
renoir_random_forest(
x,
y = NULL,
weights = NULL,
offset = NULL,
resp.type,
observations = NULL,
features = NULL,
ntree = 500,
clean = FALSE,
keep.call = TRUE,
...
)Arguments
- x
the input matrix, where rows are observations and columns are variables
- y
the response variable. Its number of rows must match the number of rows of
x- weights
priors of the observations
- offset
used only for GLM methods, it is an a priori known component to be included in the linear predictor during fitting
- resp.type
the response type
- observations
indices of observations to keep
- features
indices of predictors to keep
- ntree
Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
- ...
further arguments to
randomForest
Value
An object of class Trained