This vignette will guide you through its basic usage. It will show how to build a simple binary classification model based on a subset of the `bank` dataset (Moro, Cortez, and Rita 2014). You will use the two input features "age" and "balance" to predict whether a client has subscribed a term deposit.
## The dataset
The dataset looks as follows.
```{r}
data(bank, package = "lightgbm")
bank[1L:5L, c("y", "age", "balance")]
# Distribution of the response
table(bank$y)
```
## Training the model
The R package of LightGBM offers two functions to train a model:
- `lgb.train()`: This is the main training logic. It offers full flexibility but requires a `Dataset` object created by the `lgb.Dataset()` function.
- `lightgbm()`: Simpler, but less flexible. Data can be passed without having to bother with `lgb.Dataset()`.
### Using the `lightgbm()` function
In a first step, you need to convert data to numeric. Afterwards, you are ready to fit the model by the `lightgbm()` function.
It seems to have worked! And the predictions are indeed probabilities between 0 and 1.
### Using the `lgb.train()` function
Alternatively, you can go for the more flexible interface `lgb.train()`. Here, as an additional step, you need to prepare `y` and `X` by the data API `lgb.Dataset()` of LightGBM. Parameters are passed to `lgb.train()` as a named list.
```{r}
# Data interface
dtrain <- lgb.Dataset(X, label = y)
# Parameters
params <- list(
objective = "binary"
, num_leaves = 4L
, learning_rate = 1.0
)
# Train
fit <- lgb.train(
params
, data = dtrain
, nrounds = 10L
, verbose = -1L
)
```
Try it out! If stuck, visit LightGBM's [documentation](https://lightgbm.readthedocs.io/en/latest/R/index.html) for more details.
```{r, echo = FALSE, results = "hide"}
# Cleanup
if (file.exists("lightgbm.model")) {
file.remove("lightgbm.model")
}
```
## References
Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." In Advances in Neural Information Processing Systems 30 (NIPS 2017).
Moro, Sérgio, Paulo Cortez, and Paulo Rita. 2014. "A Data-Driven Approach to Predict the Success of Bank Telemarketing." Decision Support Systems 62: 22–31.