Retrieve either a single or many Gains/Lift tables from H2O objects.
h2o.gainsLift(object, ...)
h2o.gains_lift(object, ...)
# S4 method for class 'H2OModel'
h2o.gainsLift(object, newdata, valid = FALSE, xval = FALSE, ...)
# S4 method for class 'H2OModelMetrics'
h2o.gainsLift(object)Either an H2OModel object or an H2OModelMetrics object.
further arguments to be passed to/from this method.
An H2OFrame object that can be scored on. Requires a valid response column.
Retrieve the validation metric.
Retrieve the cross-validation metric.
Calling this function on H2OModel objects returns a
Gains/Lift table corresponding to the predict function.
The H2OModelMetrics version of this function will only take H2OBinomialMetrics objects.
predict for generating prediction frames,
h2o.performance for creating
H2OModelMetrics.
if (FALSE) { # \dontrun{
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.uploadFile(prostate_path)
prostate[, 2] <- as.factor(prostate[, 2])
model <- h2o.gbm(x = 3:9, y = 2, distribution = "bernoulli",
training_frame = prostate, validation_frame = prostate, nfolds = 3)
h2o.gainsLift(model) ## extract training metrics
h2o.gainsLift(model, valid = TRUE) ## extract validation metrics (here: the same)
h2o.gainsLift(model, xval = TRUE) ## extract cross-validation metrics
h2o.gainsLift(model, newdata = prostate) ## score on new data (here: the same)
# Generating a ModelMetrics object
perf <- h2o.performance(model, prostate)
h2o.gainsLift(perf) ## extract from existing metrics object
} # }