Retrieve either a single or many Gains/Lift tables from H2O objects.
h2o.gainsLift(object, ...) h2o.gains_lift(object, ...) # S4 method for H2OModel h2o.gainsLift(object, newdata, valid = FALSE, xval = FALSE, ...) # S4 method for H2OModelMetrics h2o.gainsLift(object)
| object | Either an H2OModel object or an H2OModelMetrics object. |
|---|---|
| … | further arguments to be passed to/from this method. |
| newdata | An H2OFrame object that can be scored on. Requires a valid response column. |
| valid | Retrieve the validation metric. |
| xval | 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.
# NOT RUN { 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 # }