bestMpTuneModel: methods for 'bestMpTuneModel', 'resampleMpTune'

In linxihui/lazyML: Automatic machine learning algorithms and hyper-parameters selection

Description

predict, print and resample consistency checking methods for 'bestMpTuneModel', 'resampledMpTune'

Usage

## S3 method for class 'bestMpTuneModel.formula'
predict(object, newdata, type = c("raw",
  "prob"), ...)

## S3 method for class 'bestMpTuneModel'
predict(object, newdata, type = c("raw", "prob"),
  ...)

## S3 method for class 'bestMpTuneModel'
print(x, digits = 4, row.names = FALSE, ...)

## S3 method for class 'resampledMpTune'
print(x, digits = 4, ...)

checkConsistency(object)

Arguments

object	A 'bestMpTuneModel' or 'resampleMpTune'
newdata	A new design matrix if object is 'bestMpTuneModel' or a new data frame if object is 'bestMpTuneModel.formula'
type	A string indicated type of prediction, only for classification and possibly survival.
...	Just for campatibility
x	A 'bestMpTuneModel' or 'resampleMpTune'
digits	Number of effective digits to keep for numeric variables
row.names	Should row names / numbers be printed

Details

For predict, it depends on problem types and models,

• 'classifiction' a vector of predictions if type = 'raw' and a matrix where each coloumn is predicted probabilities for an each class named by its class label if type = 'prob

• 'regression' it is always a vector of prediction

• 'survival' if type = 'raw', return a vector of 'risk' or predicted survival time (depending on models). If type = 'prob', for some model (e.g., randomForestSRC::rfsrc), it returns a matrix of survival probabilities with each columns named by survival times

. Function checkConsistency checks on the consistency of resampling in 'resampledMpTune

Value

prediction vector or matrix, performance summary, consistency indicators

See Also

See mpTune for examples

linxihui/lazyML documentation built on May 21, 2019, 6:39 a.m.