`train_lightgbm`

is a wrapper for `lightgbm`

tree-based models
where all of the model arguments are in the main function.

## Usage

```
train_lightgbm(
x,
y,
max_depth = -1,
num_iterations = 100,
learning_rate = 0.1,
feature_fraction_bynode = 1,
min_data_in_leaf = 20,
min_gain_to_split = 0,
bagging_fraction = 1,
early_stopping_round = NULL,
validation = 0,
counts = TRUE,
quiet = FALSE,
...
)
```

## Arguments

- x
A data frame or matrix of predictors

- y
A vector (factor or numeric) or matrix (numeric) of outcome data.

- max_depth
An integer for the maximum depth of the tree.

- num_iterations
An integer for the number of boosting iterations.

- learning_rate
A numeric value between zero and one to control the learning rate.

- feature_fraction_bynode
Fraction of predictors that will be randomly sampled at each split.

- min_data_in_leaf
A numeric value for the minimum sum of instances needed in a child to continue to split.

- min_gain_to_split
A number for the minimum loss reduction required to make a further partition on a leaf node of the tree.

- bagging_fraction
Subsampling proportion of rows. Setting this argument to a non-default value will also set

`bagging_freq = 1`

. See the Bagging section in`?details_boost_tree_lightgbm`

for more details.- early_stopping_round
Number of iterations without an improvement in the objective function occur before training should be halted.

- validation
The

*proportion*of the training data that are used for performance assessment and potential early stopping.- counts
A logical; should

`feature_fraction_bynode`

be interpreted as the*number*of predictors that will be randomly sampled at each split?`TRUE`

indicates that`mtry`

will be interpreted in its sense as a*count*,`FALSE`

indicates that the argument will be interpreted in its sense as a*proportion*.- quiet
A logical; should logging by

`lightgbm::lgb.train()`

be muted?- ...
Other options to pass to

`lightgbm::lgb.train()`

. Arguments will be correctly routed to the`param`

argument, or as a main argument, depending on their name.