Description Usage Arguments Value Author(s)
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
1 2 3 4 5 6 7 8 9 10 11 12 | 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)
|
... |
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 ? |
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 |
Che Ngufor <Ngufor.Che@mayo.edu>
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