Description Usage Arguments Value See Also Examples
View source: R/classification.R
Scalable and Flexible Gradient Boosting XGBoost is short for “Extreme Gradient Boosting”, where the term “Gradient Boosting” is proposed in the paper Greedy Function Approximation: A Gradient Boosting Machine, by Friedman. XGBoost is based on this original model. This is a function using gradient boosted trees for privacyEC.
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train.ds |
A data frame with training data and outcome labels |
holdout.ds |
A data frame with holdout data and outcome labels |
validation.ds |
A data frame with validation data and outcome labels |
label |
A character vector of the outcome variable column name |
method.model |
A character vector of the response variable type for the model |
cv.folds |
An integer for the number of cross validation folds |
num.threads |
An integer for OpenMP number of cores |
num.rounds |
An integer number of xgboost boosting iterations |
max.depth |
An integer aximum tree depth |
shrinkage |
A numeric gradient learning rate 0-1 |
objective |
A character vector for the name of the objective function in XGBoost |
save.file |
A character vector for results filename or NULL to skip |
verbose |
A flag indicating whether verbose output be sent to stdout |
A list containing:
data frame of results, a row for each update
melted results data frame for plotting
xgboost model
total elapsed time
Other classification:
epistasisRank()
,
getImportanceScores()
,
originalThresholdout()
,
privateEC()
,
privateRF()
,
standardRF()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | num.samples <- 100
num.variables <- 100
pct.signals <- 0.1
label <- "class"
sim.data <- createSimulation(num.variables = num.variables,
num.samples = num.samples,
pct.signals = pct.signals,
label = label,
sim.type = "mainEffect",
pct.train = 1 / 3,
pct.holdout = 1 / 3,
pct.validation = 1 / 3,
verbose = FALSE)
rra.results <- xgboostRF(train.ds = sim.data$train,
holdout.ds = sim.data$holdout,
validation.ds = sim.data$validation,
label = sim.data$label,
num.rounds = c(1),
max.depth = c(10),
verbose = FALSE)
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