A wrapper around mlpack::hoeffding_tree() that allows passing a formula.
Usage
hoeffding_trees(
formula = NULL,
data = NULL,
confidence_factor = 0.95,
sample_size = 10,
max_samples = 5000,
min_samples = 100,
info_gain = FALSE,
batch_mode = FALSE,
numeric_split_strategy = c("binary", "domingos"),
num_breaks = 10,
observations_before_binning = 100,
x = NULL,
y = NULL
)Arguments
- formula
A formula.
- data
A data.frame.
- confidence_factor
Confidence before splitting (between 0 and 1).
- sample_size
Number of passes to take over the dataset.
- max_samples
Maximum number of samples before splitting.
- min_samples
Minimum number of samples before splitting.
- info_gain
Logical. If set, information gain is used instead of Gini impurity for calculating Hoeffding bounds.
- batch_mode
Logical. If true, samples will be considered in batch instead of as a stream. This generally results in better trees but at the cost of memory usage and runtime.
- numeric_split_strategy
The splitting strategy to use for numeric features.
- num_breaks
If the "domingos" split strategy is used, this specifies the number of bins for each numeric split.
- observations_before_binning
If the "domingos" split strategy is used, this specifies the number of samples observed before binning is performed.
- x
Design matrix.
- y
Response matrix.