setGBMSurvival: Create setting for GBM Survival with python #' @description...

In hxia/plp-git-demo: Package for patient level prediction using data in the OMOP Common Data Model

Description

Create setting for GBM Survival with python #' @description This creates a setting for fitting GBM surivial model. You need sksurv python install. To install this open your command line and type: conda install -c sebp scikit-survival

Usage

setGBMSurvival(
  loss = "coxph",
  learningRate = 0.1,
  nEstimators = c(100),
  criterion = "friedman_mse",
  minSamplesSplit = 2,
  minSamplesLeaf = 1,
  minWeightFractionLeaf = 0,
  maxDepth = c(3, 10, 17),
  minImpuritySplit = NULL,
  minImpurityDecrease = 0,
  maxFeatures = NULL,
  maxLeafNodes = NULL,
  presort = NULL,
  subsample = 1,
  dropoutRate = 0,
  seed = NULL,
  quiet = F
)

Arguments

loss	A string specifying the loss function to minimise (default: 'coxph' )
learningRate	A double specifying the learning rate (controls convergence speed)
nEstimators	An integer specifying how many trees to build
criterion	Default: 'friedman_mse'
minSamplesSplit	An integer specifying min samples per tree split (complexity)
minSamplesLeaf	An integer specifying min samples per leaf (complexity)
minWeightFractionLeaf	Lookup
maxDepth	An integer specifying the max depth of trees (complexity)
minImpuritySplit	A double or NULL specifying the minimum impurity split
minImpurityDecrease	will add
maxFeatures	will add
maxLeafNodes	will add
presort	will add
subsample	will add
dropoutRate	will add
seed	will add
quiet	will add

Details

Pick the hyper-parameters you want to do a grid search for

Examples

## Not run: 
gbmSurv <- setGBMSurvival(learningRate=c(0.1,0.01), nEstimators =c(10,50,100),
 maxDepth=c(4,10,17), seed = 2)

## End(Not run)

hxia/plp-git-demo documentation built on March 19, 2021, 1:54 a.m.