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Jun 1, 2022
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document mtry and counts, add ?details_boost_tree_lightgbm doc #734

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c9bd1fc
add `mtry` interpretation docs
simonpcouch May 25, 2022
01ad62a
update `models.tsv` with new dev bonsai
simonpcouch May 25, 2022
0ff7cda
add `?details_boost_tree_lightgbm` doc
simonpcouch May 25, 2022
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13 changes: 13 additions & 0 deletions R/boost_tree_lightgbm.R
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#' Boosted trees via lightgbm
#'
#' [lightgbm::lgb.train()] creates a series of decision trees
#' forming an ensemble. Each tree depends on the results of previous trees.
#' All trees in the ensemble are combined to produce a final prediction.
#'
#' @includeRmd man/rmd/boost_tree_lightgbm.md details
#'
#' @name details_boost_tree_lightgbm
#' @keywords internal
NULL

# See inst/README-DOCS.md for a description of how these files are processed
2 changes: 0 additions & 2 deletions inst/models.tsv
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"C5_rules" "classification" "C5.0" "rules"
"cubist_rules" "regression" "Cubist" "rules"
"decision_tree" "censored regression" "partykit" "censored"
"decision_tree" "censored regression" "partykit" "bonsai"
"decision_tree" "censored regression" "rpart" "censored"
"decision_tree" "classification" "C5.0" NA
"decision_tree" "classification" "partykit" "bonsai"
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"proportional_hazards" "censored regression" "glmnet" "censored"
"proportional_hazards" "censored regression" "survival" "censored"
"rand_forest" "censored regression" "partykit" "censored"
"rand_forest" "censored regression" "partykit" "bonsai"
"rand_forest" "classification" "partykit" "bonsai"
"rand_forest" "classification" "randomForest" NA
"rand_forest" "classification" "ranger" NA
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186 changes: 186 additions & 0 deletions man/details_boost_tree_lightgbm.Rd
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37 changes: 31 additions & 6 deletions man/details_boost_tree_xgboost.Rd
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38 changes: 36 additions & 2 deletions man/details_rule_fit_xrf.Rd
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8 changes: 6 additions & 2 deletions man/rmd/boost_tree_lightgbm.Rmd
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Expand Up @@ -69,14 +69,18 @@ boost_tree(

Non-numeric predictors (i.e., factors) are internally converted to numeric. In the classification context, non-numeric outcomes (i.e., factors) are also internally converted to numeric.

### Interpreting `mtry`

```{r child = "template-mtry-prop.Rmd"}
```

### Verbosity

bonsai quiets much of the logging output from [lightgbm::lgb.train()] by default. With default settings, logged warnings and errors will still be passed on to the user. To print out all logs during training, set `quiet = TRUE`.

## Examples

<!-- TODO: update url to bonsai pkgdown site -->
The "Introduction to bonsai" article contains [examples](https://github.com/tidymodels/bonsai) of `boost_tree()` with the `"lightgbm"` engine.
The "Introduction to bonsai" article contains [examples](https://bonsai.tidymodels.org/articles/bonsai.html) of `boost_tree()` with the `"lightgbm"` engine.

## References

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