`sparklyr::ml_decision_tree()`

fits a model as a set of `if/then`

statements that creates a tree-based structure.

## Details

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

### Tuning Parameters

This model has 2 tuning parameters:

`tree_depth`

: Tree Depth (type: integer, default: 5L)`min_n`

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

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

```
decision_tree(tree_depth = integer(1), min_n = integer(1)) %>%
set_engine("spark") %>%
set_mode("classification") %>%
translate()
```

```
## Decision Tree Model Specification (classification)
##
## Main Arguments:
## tree_depth = integer(1)
## min_n = integer(1)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_decision_tree_classifier(x = missing_arg(), formula = missing_arg(),
## max_depth = integer(1), min_instances_per_node = min_rows(0L,
## x), seed = sample.int(10^5, 1))
```

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

```
decision_tree(tree_depth = integer(1), min_n = integer(1)) %>%
set_engine("spark") %>%
set_mode("regression") %>%
translate()
```

```
## Decision Tree Model Specification (regression)
##
## Main Arguments:
## tree_depth = integer(1)
## min_n = integer(1)
##
## Computational engine: spark
##
## Model fit template:
## sparklyr::ml_decision_tree_regressor(x = missing_arg(), formula = missing_arg(),
## max_depth = integer(1), min_instances_per_node = min_rows(0L,
## 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.

### 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.

### 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.