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#> randomForest(x = as.data.frame(x), y = y, ntree = ~2000, mtry = ~4)
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#> randomForest(x = maybe_data_frame(x), y = y, ntree = ~2000, mtry = min_cols(~4, x))
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#> Type of random forest: regression
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#> Number of trees: 2000
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#> No. of variables tried at each split: 4
@@ -149,7 +149,7 @@ rf_with_seed %>%
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Note that the call objects show `num.trees = ~2000`. The tilde is the consequence of `parsnip` using [quosures](https://adv-r.hadley.nz/evaluation.html#quosures) to process the model specification's arguments.
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Normally, when a function is executed, the function's arguments are immediately evaluated. In the case of `parsnip`, the model specification's arguments are _not_; the [expression is captured](https://www.tidyverse.org/articles/2019/04/parsnip-internals/) along with the environment where it should be evaluated. That is what a quosure does.
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Normally, when a function is executed, the function's arguments are immediately evaluated. In the case of `parsnip`, the model specification's arguments are _not_; the [expression is captured](https://www.tidyverse.org/blog/2019/04/parsnip-internals/) along with the environment where it should be evaluated. That is what a quosure does.
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`parsnip` uses these expressions to make a model fit call that is evaluated. The tilde in the call above reflects that the argument was captured using a quosure.
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