---
title: "Conventional machine learning"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Conventional machine learning}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
resource_files:
  - figures/logo.svg
  - figures/logo-dark.svg
---

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<div class="cttir-vignette-logo">
  <img class="cttir-vignette-logo-light" src="figures/logo.svg" alt="hypermodR logo" width="120" height="139" />
  <img class="cttir-vignette-logo-dark" src="figures/logo-dark.svg" alt="hypermodR logo" width="120" height="139" />
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```{r setup, include=FALSE}
library(hypermodR)
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", error = FALSE)
```
This task predicts synthetic response with grouped nearest neighbours. Subjects
are independent units; proximity in a fitted spectral space has no causal
interpretation. A fixed k is illustrative and is not selected on held-out rows.

```{r knn}
x <- hm_example()
task <- hm_task("response", "regression", "spectrum", "subject_id")
split <- hm_split(x, task, folds = 3L, inner_folds = 2L)$folds[[1L]]
w <- hm_workflow(
  task, hm_recipe(list(list(type = "scale"))),
  hm_model_spec("knn", task, list(k = 3L))
)
fit <- hm_fit(w, x, split)
selection <- list(
  observation_id = x@observations$observation_id[split$test]
)
test <- hm_spectra(x, selection)
hm_evaluate(hm_predict(fit, test), test$observations)
```

Other canonical IDs include `lda`, `qda`, `naive_bayes`, `svm`, `rf`, `xgboost`,
`gaussian_process`, `ensemble`, `kmeans`, `pam`, `dbscan`, `isolation_forest`,
`one_class_svm` and `pca_anomaly`; `coxph` and `clm` serve survival/ordinal tasks.
Inspect `hm_models(task = "classification")` and install the relevant optional
R packages explicitly. `hm_tune()` accepts explicit candidates and a nested
grouped split, learns each recipe inside inner training and refits on outer
training. Retain the tuning ledger. Compare identical held-out cases through
`hm_compare()`, with paired whole-unit resampling; report support and invalid
draws rather than selecting whichever metric looks best.

Bounded tuning currently requires Linux with `taskset`, `prlimit` and
`timeout`. Portable fitting and prediction above remain available elsewhere.

```{r bounded-platform-status}
bounded_status <- if (identical(Sys.info()[["sysname"]], "Linux")) {
  "Linux bounded tuning route available subject to installed resource tools."
} else {
  paste0(
    "Bounded tuning unsupported on this platform; portable ",
    "fitting remains available."
  )
}
cat(bounded_status)
```