Unified API for model training

Unified API for model training with parameter tuning

hanaml.train(
  data,
  method,
  trControl = NULL,
  tuneGrid = NULL,
  metric,
  param.search.strategy = NULL,
  resampling.method = NULL,
  ...
)

Arguments

data	DataFrame DataFrame containting the data.
method	character Name of HANA ML functions.
trControl	train.control Pass train.control function.
tuneGrid	param.grid Pass param.grid function.
metric	character Scoring metric. Identical to evaluation.metric in HANA ML functions. then no model evaluation or parameter selection will be activated. No default value.
param.search.strategy	c("grid", "random"), optional Specifies the method to activate parameter selection. If not specified, model parameter selection shall not be triggered.
resampling.method	character, optional Specifies the resampling values.
...	Reserved parameter.

Value

a HANA ML object.

Examples

> trctrl <- hanaml.trainControl(resampling.method = "cv",
                                param.search.strategy = "grid",
                                fold.num = 2, repeat.times = 2,
                                random.state = 1,
                                progress.indicator.id = "TEST")
> paramgrid <- hanaml.expand.grid(enet.lambda = c(1e-2, 1e-3, 0.05),
                                enet.alpha = c(0.1, 0.5, 0.03))
> lr <-  hanaml.train(data = df.fit.enet,
                      method = "LinearRegression",
                      trControl = trctrl,
                      tuneGrid = paramgrid,
                      metric = "rmse",
                      key = "ID", label = "Y",
                      solver = "admm")

See also

hanaml.range