# Random Forest Model for Regression and Classification

`spark.randomForest.Rd`

`spark.randomForest`

fits a Random Forest Regression model or Classification model on
a SparkDataFrame. Users can call `summary`

to get a summary of the fitted Random Forest
model, `predict`

to make predictions on new data, and `write.ml`

/`read.ml`

to
save/load fitted models.
For more details, see
Random Forest Regression and
Random Forest Classification

## Usage

```
spark.randomForest(data, formula, ...)
# S4 method for class 'SparkDataFrame,formula'
spark.randomForest(
data,
formula,
type = c("regression", "classification"),
maxDepth = 5,
maxBins = 32,
numTrees = 20,
impurity = NULL,
featureSubsetStrategy = "auto",
seed = NULL,
subsamplingRate = 1,
minInstancesPerNode = 1,
minInfoGain = 0,
checkpointInterval = 10,
maxMemoryInMB = 256,
cacheNodeIds = FALSE,
handleInvalid = c("error", "keep", "skip"),
bootstrap = TRUE
)
# S4 method for class 'RandomForestRegressionModel'
summary(object)
# S3 method for class 'summary.RandomForestRegressionModel'
print(x, ...)
# S4 method for class 'RandomForestClassificationModel'
summary(object)
# S3 method for class 'summary.RandomForestClassificationModel'
print(x, ...)
# S4 method for class 'RandomForestRegressionModel'
predict(object, newData)
# S4 method for class 'RandomForestClassificationModel'
predict(object, newData)
# S4 method for class 'RandomForestRegressionModel,character'
write.ml(object, path, overwrite = FALSE)
# S4 method for class 'RandomForestClassificationModel,character'
write.ml(object, path, overwrite = FALSE)
```

## Arguments

- data
a SparkDataFrame for training.

- formula
a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', ':', '+', and '-'.

- ...
additional arguments passed to the method.

- type
type of model, one of "regression" or "classification", to fit

- maxDepth
Maximum depth of the tree (>= 0).

- maxBins
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. Must be >= 2 and >= number of categories in any categorical feature.

- numTrees
Number of trees to train (>= 1).

- impurity
Criterion used for information gain calculation. For regression, must be "variance". For classification, must be one of "entropy" and "gini", default is "gini".

- featureSubsetStrategy
The number of features to consider for splits at each tree node. Supported options: "auto" (choose automatically for task: If numTrees == 1, set to "all." If numTrees > 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 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 is "auto".

- seed
integer seed for random number generation.

- subsamplingRate
Fraction of the training data used for learning each decision tree, in range (0, 1].

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

- minInfoGain
Minimum information gain for a split to be considered at a tree node.

- checkpointInterval
Param for set checkpoint interval (>= 1) or disable checkpoint (-1). Note: this setting will be ignored if the checkpoint directory is not set.

- maxMemoryInMB
Maximum memory in MiB allocated to histogram aggregation.

- cacheNodeIds
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. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval.

- handleInvalid
How to handle invalid data (unseen labels or NULL values) in features and label column of string type in classification model. Supported options: "skip" (filter out rows with invalid data), "error" (throw an error), "keep" (put invalid data in a special additional bucket, at index numLabels). Default is "error".

- bootstrap
Whether bootstrap samples are used when building trees.

- object
A fitted Random Forest regression model or classification model.

- x
summary object of Random Forest regression model or classification model returned by

`summary`

.- newData
a SparkDataFrame for testing.

- path
The directory where the model is saved.

- overwrite
Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.

## Value

`spark.randomForest`

returns a fitted Random Forest model.

`summary`

returns summary information of the fitted model, which is a list.
The list of components includes `formula`

(formula),
`numFeatures`

(number of features), `features`

(list of features),
`featureImportances`

(feature importances), `maxDepth`

(max depth of trees),
`numTrees`

(number of trees), and `treeWeights`

(tree weights).

`predict`

returns a SparkDataFrame containing predicted labeled in a column named
"prediction".

## Note

spark.randomForest since 2.1.0

summary(RandomForestRegressionModel) since 2.1.0

print.summary.RandomForestRegressionModel since 2.1.0

summary(RandomForestClassificationModel) since 2.1.0

print.summary.RandomForestClassificationModel since 2.1.0

predict(RandomForestRegressionModel) since 2.1.0

predict(RandomForestClassificationModel) since 2.1.0

write.ml(RandomForestRegressionModel, character) since 2.1.0

write.ml(RandomForestClassificationModel, character) since 2.1.0

## Examples

```
if (FALSE) { # \dontrun{
# fit a Random Forest Regression Model
df <- createDataFrame(longley)
model <- spark.randomForest(df, Employed ~ ., type = "regression", maxDepth = 5, maxBins = 16)
# get the summary of the model
summary(model)
# make predictions
predictions <- predict(model, df)
# save and load the model
path <- "path/to/model"
write.ml(model, path)
savedModel <- read.ml(path)
summary(savedModel)
# fit a Random Forest Classification Model
t <- as.data.frame(Titanic)
df <- createDataFrame(t)
model <- spark.randomForest(df, Survived ~ Freq + Age, "classification")
} # }
```