# ml_random_forest_classifier

##### Spark ML -- Random Forest

Perform classification and regression using random forests.

##### Usage

```
ml_random_forest_classifier(
x,
formula = NULL,
num_trees = 20,
subsampling_rate = 1,
max_depth = 5,
min_instances_per_node = 1,
feature_subset_strategy = "auto",
impurity = "gini",
min_info_gain = 0,
max_bins = 32,
seed = NULL,
thresholds = NULL,
checkpoint_interval = 10,
cache_node_ids = FALSE,
max_memory_in_mb = 256,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("random_forest_classifier_"),
...
)
```ml_random_forest(
x,
formula = NULL,
type = c("auto", "regression", "classification"),
features_col = "features",
label_col = "label",
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
feature_subset_strategy = "auto",
impurity = "auto",
checkpoint_interval = 10,
max_bins = 32,
max_depth = 5,
num_trees = 20,
min_info_gain = 0,
min_instances_per_node = 1,
subsampling_rate = 1,
seed = NULL,
thresholds = NULL,
cache_node_ids = FALSE,
max_memory_in_mb = 256,
uid = random_string("random_forest_"),
response = NULL,
features = NULL,
...
)

ml_random_forest_regressor(
x,
formula = NULL,
num_trees = 20,
subsampling_rate = 1,
max_depth = 5,
min_instances_per_node = 1,
feature_subset_strategy = "auto",
impurity = "variance",
min_info_gain = 0,
max_bins = 32,
seed = NULL,
checkpoint_interval = 10,
cache_node_ids = FALSE,
max_memory_in_mb = 256,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
uid = random_string("random_forest_regressor_"),
...
)

##### Arguments

- x
A

`spark_connection`

,`ml_pipeline`

, or a`tbl_spark`

.- formula
Used when

`x`

is a`tbl_spark`

. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.- num_trees
Number of trees to train (>= 1). If 1, then no bootstrapping is used. If > 1, then bootstrapping is done.

- subsampling_rate
Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

- max_depth
Maximum depth of the tree (>= 0); that is, the maximum number of nodes separating any leaves from the root of the tree.

- min_instances_per_node
Minimum number of instances each child must have after split.

- feature_subset_strategy
The number of features to consider for splits at each tree node. See details for options.

- impurity
Criterion used for information gain calculation. Supported: "entropy" and "gini" (default) for classification and "variance" (default) for regression. For

`ml_decision_tree`

, setting`"auto"`

will default to the appropriate criterion based on model type.- min_info_gain
Minimum information gain for a split to be considered at a tree node. Should be >= 0, defaults to 0.

- max_bins
The maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity.

- seed
Seed for random numbers.

- thresholds
Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value

`p/t`

is predicted, where`p`

is the original probability of that class and`t`

is the class's threshold.- checkpoint_interval
Set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations, defaults to 10.

- cache_node_ids
If

`FALSE`

, the algorithm will pass trees to executors to match instances with nodes. If`TRUE`

, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Defaults to`FALSE`

.- max_memory_in_mb
Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. Defaults to 256.

- features_col
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by

`ft_r_formula`

.- label_col
Label column name. The column should be a numeric column. Usually this column is output by

`ft_r_formula`

.- prediction_col
Prediction column name.

- probability_col
Column name for predicted class conditional probabilities.

- raw_prediction_col
Raw prediction (a.k.a. confidence) column name.

- uid
A character string used to uniquely identify the ML estimator.

- ...
Optional arguments; see Details.

- type
The type of model to fit.

`"regression"`

treats the response as a continuous variable, while`"classification"`

treats the response as a categorical variable. When`"auto"`

is used, the model type is inferred based on the response variable type -- if it is a numeric type, then regression is used; classification otherwise.- response
(Deprecated) The name of the response column (as a length-one character vector.)

- features
(Deprecated) The name of features (terms) to use for the model fit.

##### Details

When `x`

is a `tbl_spark`

and `formula`

(alternatively, `response`

and `features`

) is specified, the function returns a `ml_model`

object wrapping a `ml_pipeline_model`

which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument `predicted_label_col`

(defaults to `"predicted_label"`

) can be used to specify the name of the predicted label column. In addition to the fitted `ml_pipeline_model`

, `ml_model`

objects also contain a `ml_pipeline`

object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by `ml_save`

with `type = "pipeline"`

to faciliate model refresh workflows.

The supported options for `feature_subset_strategy`

are

`"auto"`

: Choose automatically for task: If`num_trees == 1`

, set to`"all"`

. If`num_trees > 1`

(forest), set to`"sqrt"`

for classification and to`"onethird"`

for regression.`"all"`

: use all features`"onethird"`

: use 1/3 of the features`"sqrt"`

: use use sqrt(number of features)`"log2"`

: use log2(number of features)`"n"`

: when`n`

is in the range (0, 1.0], use n * number of features. When`n`

is in the range (1, number of features), use`n`

features. (default =`"auto"`

)

`ml_random_forest`

is a wrapper around `ml_random_forest_regressor.tbl_spark`

and `ml_random_forest_classifier.tbl_spark`

and calls the appropriate method based on model type.

##### Value

The object returned depends on the class of `x`

.

`spark_connection`

: When`x`

is a`spark_connection`

, the function returns an instance of a`ml_estimator`

object. The object contains a pointer to a Spark`Predictor`

object and can be used to compose`Pipeline`

objects.`ml_pipeline`

: When`x`

is a`ml_pipeline`

, the function returns a`ml_pipeline`

with the predictor appended to the pipeline.`tbl_spark`

: When`x`

is a`tbl_spark`

, a predictor is constructed then immediately fit with the input`tbl_spark`

, returning a prediction model.`tbl_spark`

, with`formula`

: specified When`formula`

is specified, the input`tbl_spark`

is first transformed using a`RFormula`

transformer before being fit by the predictor. The object returned in this case is a`ml_model`

which is a wrapper of a`ml_pipeline_model`

.

##### See Also

See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.

Other ml algorithms:
`ml_aft_survival_regression()`

,
`ml_decision_tree_classifier()`

,
`ml_gbt_classifier()`

,
`ml_generalized_linear_regression()`

,
`ml_isotonic_regression()`

,
`ml_linear_regression()`

,
`ml_linear_svc()`

,
`ml_logistic_regression()`

,
`ml_multilayer_perceptron_classifier()`

,
`ml_naive_bayes()`

,
`ml_one_vs_rest()`

##### Examples

```
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
partitions <- iris_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
iris_training <- partitions$training
iris_test <- partitions$test
rf_model <- iris_training %>%
ml_random_forest(Species ~ ., type = "classification")
pred <- ml_predict(rf_model, iris_test)
ml_multiclass_classification_evaluator(pred)
# }
```

*Documentation reproduced from package sparklyr, version 1.4.0, License: Apache License 2.0 | file LICENSE*