# Basic Walkthrough

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## 1. Introduction

Welcome to the world of LightGBM, a highly efficient gradient boosting implementation (Ke et al. 2017).

    library(lightgbm)

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.

## 2. The dataset

The dataset looks as follows.

    data(bank, package = "lightgbm")

    bank[1L:5L, c("y", "age", "balance")]
    #>     y age balance
    #> 1: no  30    1787
    #> 2: no  33    4789
    #> 3: no  35    1350
    #> 4: no  30    1476
    #> 5: no  59       0

    # Distribution of the response
    table(bank$y)
    #>
    #>   no  yes
    #> 4000  521

## 3. 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()`.

### 3.1 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.

    # Numeric response and feature matrix
    y <- as.numeric(bank$y == "yes")
    X <- data.matrix(bank[, c("age", "balance")])

    # Train
    fit <- lightgbm(
      data = X
      , label = y
      , params = list(
        num_leaves = 4L
        , learning_rate = 1.0
        , objective = "binary"
      )
      , nrounds = 10L
      , verbose = -1L
    )

    # Result
    summary(predict(fit, X))
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    #> 0.01192 0.07370 0.09871 0.11593 0.14135 0.65796

It seems to have worked! And the predictions are indeed probabilities between 0 and 1.

### 3.2 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.

    # 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 for more details.

## 4. 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.