landmap (version 0.0.13)

tune.spLearner,spLearner-method: Optimize spLearner by fine-tuning parameters and running feature selection

Description

Optimize spLearner by fine-tuning parameters and running feature selection

Usage

# S4 method for spLearner
tune.spLearner(
  object,
  num.trees = 85,
  blocking,
  discrete_ps,
  rdesc = mlr::makeResampleDesc("CV", iters = 2L),
  inner = mlr::makeResampleDesc("Holdout"),
  maxit = 20,
  xg.model_Params,
  xg.skip = FALSE,
  parallel = "multicore",
  hzn_depth = FALSE,
  ...
)

Arguments

object

spLearner object (unoptimized),

num.trees

number of random forest trees,

blocking

blocking columns,

discrete_ps

settings for random forest,

rdesc

resampling method for fine-tuning,

inner

resampling method for feature selection,

maxit

maximum number of iterations for feature selection,

xg.model_Params

xgboost parameter set,

xg.skip

logical, should the tuning of the XGboost should be skipped?

parallel

Initiate parallel processing,

hzn_depth

specify whether horizon depth available in the training dataframe,

...

other arguments that can be passed on to mlr::makeStackedLearner,

Value

optimized object of type spLearner

Examples

# NOT RUN {
library(mlr)
library(ParamHelpers)
library(geoR)
library(xgboost)
library(kernlab)
library(ranger)
library(glmnet)
library(raster)
demo(meuse, echo=FALSE)
## Regression:
sl = c("regr.ranger", "regr.xgboost", "regr.ksvm", "regr.cvglmnet")
m <- train.spLearner(meuse["lead"], covariates=meuse.grid[,c("dist","ffreq")],
      lambda=0, parallel=FALSE, SL.library=sl)
summary(m@spModel$learner.model$super.model$learner.model)
## Optimize model:
t <- try( m0 <- tune.spLearner(m, xg.skip = TRUE, parallel=FALSE), silent=TRUE)
if(!class(t) == "try-error") summary(m0@spModel$learner.model$super.model$learner.model)
# }