`sparklyr::ml_random_forest()`

fits a model that creates a large number of
decision trees, each independent of the others. The final prediction uses all
predictions from the individual trees and combines them.

## Details

For this engine, there are multiple modes: classification and regression

### Tuning Parameters

This model has 3 tuning parameters:

`mtry`

: # Randomly Selected Predictors (type: integer, default: see below)`trees`

: # Trees (type: integer, default: 20L)`min_n`

: Minimal Node Size (type: integer, default: 1L)

`mtry`

depends on the number of columns and the model mode. The default
in `sparklyr::ml_random_forest()`

is
`floor(sqrt(ncol(x)))`

for classification and `floor(ncol(x)/3)`

for
regression.

### Translation from parsnip to the original package (regression)

```
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("spark") %>%
set_mode("regression") %>%
translate()
```

```
## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
## type = "regression", feature_subset_strategy = integer(1),
## num_trees = integer(1), min_instances_per_node = min_rows(~integer(1),
## x), seed = sample.int(10^5, 1))
```

`min_rows()`

and `min_cols()`

will adjust the number of neighbors if the
chosen value if it is not consistent with the actual data dimensions.

### Translation from parsnip to the original package (classification)

```
rand_forest(
mtry = integer(1),
trees = integer(1),
min_n = integer(1)
) %>%
set_engine("spark") %>%
set_mode("classification") %>%
translate()
```

```
## Random Forest Model Specification (classification)
##
## Main Arguments:
## mtry = integer(1)
## trees = integer(1)
## min_n = integer(1)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_random_forest(x = missing_arg(), formula = missing_arg(),
## type = "classification", feature_subset_strategy = integer(1),
## num_trees = integer(1), min_instances_per_node = min_rows(~integer(1),
## x), seed = sample.int(10^5, 1))
```

### Preprocessing requirements

This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. `{a, c}`

vs `{b, d}`

) when splitting at a node. Dummy variables
are not required for this model.

### Other details

For models created using the `"spark"`

engine, there are several things
to consider.

Only the formula interface to via

`fit()`

is available; using`fit_xy()`

will generate an error.The predictions will always be in a Spark table format. The names will be the same as documented but without the dots.

There is no equivalent to factor columns in Spark tables so class predictions are returned as character columns.

To retain the model object for a new R session (via

`save()`

), the`model$fit`

element of the parsnip object should be serialized via`ml_save(object$fit)`

and separately saved to disk. In a new session, the object can be reloaded and reattached to the parsnip object.

### Case weights

This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on `tidymodels.org`

.

The `fit()`

and `fit_xy()`

arguments have arguments called
`case_weights`

that expect vectors of case weights.

Note that, for spark engines, the `case_weight`

argument value should be
a character string to specify the column with the numeric case weights.