---
title: "Getting started with YARF"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with YARF}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

YARF fits regression and classification forests while exposing the splitting,
node-assignment, aggregation, and out-of-bag calculations for customization.
Java 8 or newer is required.

## Fit a classification forest

```{r classification}
library(YARF)
set_YARF_num_cores(1)

fit = YARF(
  X = iris[, 1:4],
  y = iris$Species,
  num_trees = 50,
  seed = 2026
)

pred = predict(fit, iris[, 1:4])
table(observed = iris$Species, predicted = pred)
```

## Inspect out-of-bag performance

```{r oob}
fit = YARF_update_with_oob_results(fit)
summary(fit)
query_variable_proportions_across_trees(fit)
```

## Missing values

The default `use_missing_data = TRUE` enables missingness incorporated in
attributes. For iterative random-forest imputation, use `YARFMissForest()`.

```{r missing}
X_missing = iris[, 1:4]
X_missing[1:5, 1] = NA

fit_missing = YARF(
  X = X_missing,
  y = iris$Species,
  num_trees = 50,
  seed = 2026,
  use_missing_data = TRUE
)
```

See the function reference for custom JavaScript hooks and tree-inspection
utilities.