wehub-resource-sync
128 lines
3.4 KiB
Plaintext
128 lines
3.4 KiB
Plaintext
---
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title:
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"Basic Walkthrough"
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description: >
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This vignette describes how to train a LightGBM model for binary classification.
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output:
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markdown::html_format:
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options:
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toc: true
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number_sections: true
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vignette: >
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%\VignetteIndexEntry{Basic Walkthrough}
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%\VignetteEngine{knitr::knitr}
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%\VignetteEncoding{UTF-8}
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---
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```{r, include = FALSE}
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knitr::opts_chunk$set(
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collapse = TRUE
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, comment = "#>"
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, warning = FALSE
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, message = FALSE
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)
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```
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## Introduction
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Welcome to the world of [LightGBM](https://lightgbm.readthedocs.io/en/latest/), a highly efficient gradient boosting implementation (Ke et al. 2017).
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```{r}
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library(lightgbm)
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```
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```{r, include=FALSE}
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# limit number of threads used, to be respectful of CRAN's resources when it checks this vignette
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data.table::setDTthreads(1L)
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setLGBMthreads(2L)
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```
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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.
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## The dataset
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The dataset looks as follows.
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```{r}
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data(bank, package = "lightgbm")
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bank[1L:5L, c("y", "age", "balance")]
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# Distribution of the response
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table(bank$y)
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```
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## Training the model
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The R-package of LightGBM offers two functions to train a model:
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- `lgb.train()`: This is the main training logic. It offers full flexibility but requires a `Dataset` object created by the `lgb.Dataset()` function.
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- `lightgbm()`: Simpler, but less flexible. Data can be passed without having to bother with `lgb.Dataset()`.
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### Using the `lightgbm()` function
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In a first step, you need to convert data to numeric. Afterwards, you are ready to fit the model by the `lightgbm()` function.
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```{r}
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# Numeric response and feature matrix
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y <- as.numeric(bank$y == "yes")
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X <- data.matrix(bank[, c("age", "balance")])
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# Train
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fit <- lightgbm(
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data = X
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, label = y
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, params = list(
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num_leaves = 4L
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, learning_rate = 1.0
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, objective = "binary"
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)
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, nrounds = 10L
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, verbose = -1L
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)
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# Result
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summary(predict(fit, X))
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```
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It seems to have worked! And the predictions are indeed probabilities between 0 and 1.
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### Using the `lgb.train()` function
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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.
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```{r}
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# Data interface
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dtrain <- lgb.Dataset(X, label = y)
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# Parameters
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params <- list(
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objective = "binary"
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, num_leaves = 4L
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, learning_rate = 1.0
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)
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# Train
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fit <- lgb.train(
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params
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, data = dtrain
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, nrounds = 10L
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, verbose = -1L
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)
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```
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Try it out! If stuck, visit LightGBM's [documentation](https://lightgbm.readthedocs.io/en/latest/R/index.html) for more details.
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```{r, echo = FALSE, results = "hide"}
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# Cleanup
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if (file.exists("lightgbm.model")) {
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file.remove("lightgbm.model")
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}
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```
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## References
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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).
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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.
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