Train: Train, Validate and Test various models: Functions to train,...

In nguforche/Vira: Virtual Inteligent Robot Assistant

Description

Train, Validate and Test various models: Functions to train, select best hyper-paramters, select best classification thresholds and evaluate final model on the test set using a number of machine learning classification algorithms

Usage

TrainModels(...)

Train.Test(...)

Train.Boot(classifier, nboots = 2, XY.dat, resp.vars, rhs.vars,
  part.vars, reg.vars, para, parallel = TRUE, n.cores = 2,
  seed = 12345678)

cv.gbm(X, y, outcome = NULL, n.trees = seq(150, 500, by = 10),
  interaction.depth, n.minobsinnode = 5, shrinkage = 10^(seq(-3, -1,
  length = 10)), bag.fraction = 0.5, distribution = "bernoulli",
  foldid = NULL, nfolds = 10, seed = 220, verbose = FALSE)

Arguments

...	further arguments passed to or from other methods.
classifier	character list of classification models. See names(TrainAllModels()).
para	named list of model parameters
X.trn, X.val, X.tst	matrix of predictors
Y.trn, Y.val, Y.tst	matrix of binary {0,1} response variable
varimp	(logical) compute variable importance ?
opt.para	(logical) tune parameters ?
return.model	(logical) return trained model ?

Value

TrainModels returns a list of functions for training various algorithms. Currently 7 machine learning algorithms ELR, GLM, GLMnet, RF, GBM, avNNET, and SVM. Others can be easily added. Train.Validate.Test returns a list of performance measures for each classifier traoned:

model	trained model
para	named list of model hyper-paramters (tunned values if opt.para = TRUE)
run.time	compute time
varimp	variable importance if classifier is GLM, GBM or RF
perf	a list with two data.frames: val.perf and tst.perf containing performance measures for validation and test sets

Author(s)

Che Ngufor <[e-mail]>

nguforche/Vira documentation built on June 2, 2019, 9:08 p.m.