SparkR (version 3.1.2)

spark.decisionTree: Decision Tree Model for Regression and Classification

Description

spark.decisionTree fits a Decision Tree Regression model or Classification model on a SparkDataFrame. Users can call summary to get a summary of the fitted Decision Tree model, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models. For more details, see Decision Tree Regression and Decision Tree Classification

Usage

spark.decisionTree(data, formula, ...)
# S4 method for SparkDataFrame,formula
spark.decisionTree(
  data,
  formula,
  type = c("regression", "classification"),
  maxDepth = 5,
  maxBins = 32,
  impurity = NULL,
  seed = NULL,
  minInstancesPerNode = 1,
  minInfoGain = 0,
  checkpointInterval = 10,
  maxMemoryInMB = 256,
  cacheNodeIds = FALSE,
  handleInvalid = c("error", "keep", "skip")
)
# S4 method for DecisionTreeRegressionModel
summary(object)
# S3 method for summary.DecisionTreeRegressionModel
print(x, ...)
# S4 method for DecisionTreeClassificationModel
summary(object)
# S3 method for summary.DecisionTreeClassificationModel
print(x, ...)
# S4 method for DecisionTreeRegressionModel
predict(object, newData)
# S4 method for DecisionTreeClassificationModel
predict(object, newData)
# S4 method for DecisionTreeRegressionModel,character
write.ml(object, path, overwrite = FALSE)
# S4 method for DecisionTreeClassificationModel,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.

impurity

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

seed

integer seed for random number generation.

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".

object

A fitted Decision Tree regression model or classification model.

x

summary object of Decision Tree 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.decisionTree returns a fitted Decision Tree 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), and maxDepth (max depth of trees).

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

Examples

# NOT RUN {
# fit a Decision Tree Regression Model
df <- createDataFrame(longley)
model <- spark.decisionTree(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 Decision Tree Classification Model
t <- as.data.frame(Titanic)
df <- createDataFrame(t)
model <- spark.decisionTree(df, Survived ~ Freq + Age, "classification")
# }