Description Usage Arguments Value Note Examples

`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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
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)
``` |

`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. |

`ml_gbt`

returns a fitted Gradient Boosted Tree model.

ml_gbt since 2.1.0

write_ml(GBTRegressionModel, character) since 2.1.0

write_ml(GBTClassificationModel, character) since 2.1.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
## 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)
``` |

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