Gradient Boosted Tree Model for Regression and Classification

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

Usage

spark.gbt(data, formula, ...)

# S4 method for SparkDataFrame,formula
spark.gbt(
  data,
  formula,
  type = c("regression", "classification"),
  maxDepth = 5,
  maxBins = 32,
  maxIter = 20,
  stepSize = 0.1,
  lossType = NULL,
  seed = NULL,
  subsamplingRate = 1,
  minInstancesPerNode = 1,
  minInfoGain = 0,
  checkpointInterval = 10,
  maxMemoryInMB = 256,
  cacheNodeIds = FALSE,
  handleInvalid = c("error", "keep", "skip")
)

# S4 method for GBTRegressionModel
summary(object)

# S3 method for summary.GBTRegressionModel
print(x, ...)

# S4 method for GBTClassificationModel
summary(object)

# S3 method for summary.GBTClassificationModel
print(x, ...)

# S4 method for GBTRegressionModel
predict(object, newData)

# S4 method for GBTClassificationModel
predict(object, newData)

# S4 method for GBTRegressionModel,character
write.ml(object, path, overwrite = FALSE)

# S4 method for GBTClassificationModel,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.
maxIter: Param for maximum number of iterations (>= 0).
stepSize: Param for Step size to be used for each iteration of optimization.
lossType: Loss function which GBT tries to minimize. For classification, must be "logistic". For regression, must be one of "squared" (L2) and "absolute" (L1), default is "squared".
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. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1.
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 Gradient Boosted Tree regression model or classification model.
x: summary object of Gradient Boosted 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.gbt returns a fitted Gradient Boosted 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), 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.gbt since 2.1.0

summary(GBTRegressionModel) since 2.1.0

print.summary.GBTRegressionModel since 2.1.0

summary(GBTClassificationModel) since 2.1.0

print.summary.GBTClassificationModel since 2.1.0

predict(GBTRegressionModel) since 2.1.0

predict(GBTClassificationModel) since 2.1.0

write.ml(GBTRegressionModel, character) since 2.1.0

write.ml(GBTClassificationModel, character) since 2.1.0

Examples

if (FALSE) {
# fit a Gradient Boosted Tree Regression Model
df <- createDataFrame(longley)
model <- spark.gbt(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 Gradient Boosted Tree Classification Model
# label must be binary - Only binary classification is supported for GBT.
t <- as.data.frame(Titanic)
df <- createDataFrame(t)
model <- spark.gbt(df, Survived ~ Age + Freq, "classification")

# numeric label is also supported
t2 <- as.data.frame(Titanic)
t2$NumericGender <- ifelse(t2$Sex == "Male", 0, 1)
df <- createDataFrame(t2)
model <- spark.gbt(df, NumericGender ~ ., type = "classification")
}