ml_gbt: Gradient Boosted Tree Model for Regression and Classification

In danzafar/tidyspark: A Tidy Interface to Spark

Description

ml_gbt fits a Gradient Boosted Tree Regression model or Classification model on a spark_tbl. 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

ml_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 signature 'GBTRegressionModel,character'
write_ml(object, path, overwrite = FALSE)

## S4 method for signature 'GBTClassificationModel,character'
write_ml(object, path, overwrite = FALSE)

Arguments

data	a spark_tbl for training.
formula	a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', ':', '+', and '-'.
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 MB 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.
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.
...	additional arguments passed to the method.

Value

ml_gbt returns a fitted Gradient Boosted Tree model.

Note

ml_gbt since 2.1.0

write_ml(GBTRegressionModel, character) since 2.1.0

write_ml(GBTClassificationModel, character) since 2.1.0

Examples

## Not run: 
# fit a Gradient Boosted Tree Regression Model
df <- spark_tbl(longley)
model <- df %>%
  ml_gbt(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 <- spark_tbl(t)
model <- ml_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 <- spark_tbl(t2)
model <- ml_gbt(df, NumericGender ~ ., type = "classification")

## End(Not run)

danzafar/tidyspark documentation built on Sept. 30, 2020, 12:19 p.m.