---
title: "Classification"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Classification}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

`objective = "binary"` fits a logistic classification model. The response
must be 0/1 (numeric or logical) or a two-level factor.

```{r}
library(fastgbm)

x <- as.matrix(mtcars[, c("mpg", "disp", "hp", "wt")])
y <- mtcars$am  # 0 = automatic, 1 = manual

fit <- fastgbm(
  x, y = y, objective = "binary",
  ntrees = 100L, learning_rate = 0.1, max_depth = 3L,
  seed = 1L, verbose = FALSE
)
fit
```

`objective` can be omitted: `fastgbm()` defaults to `"binary"` whenever `y`
is a 0/1 vector (or two-level factor).

## Predictions and evaluation

```{r}
prob <- predict(fit, x, type = "response")  # predicted probabilities
head(prob)

link <- predict(fit, x, type = "link")      # log-odds
head(link)

metrics(fit, y = y)  # log loss
mean((prob > 0.5) == y)  # training accuracy
importance(fit)
```

## Formula interface

```{r}
dat <- mtcars
dat$am <- factor(dat$am)
fit2 <- fastgbm(am ~ mpg + disp + hp + wt, data = dat, ntrees = 100L, verbose = FALSE)
```