Three applications of caret package: Modeling and parameter optimization

Original address: http://xccds.github.io/2011/09/caret_1976.html/

When modeling, the parameters of the model need to be optimized, and the main function commands in the caret package are train.

First we get the sample data after feature selection, and divide it into training samples and test samples.
Newdata4=newdata3[,profile$optvariables]
Intrain = Createdatapartition (mdrrclass, p = 3/4, list = FALSE)
Trainx = Newdata4[intrain,]
TESTX = Newdata4[-intrain,]
Trainy = Mdrrclass[intrain]
testy = Mdrrclass[-intrain] Then define the model training parameters, method determine the multiple cross-examination of the sampling methods, number determined the division of the weight, repeats determined the number of repetitions.

Fitcontrol = Traincontrol (method = "REPEATEDCV", Number = ten, repeats = 3,returnresamp = "All") determines the parameter selection range, in this case the model is prepared using the GBM algorithm, the corresponding The parameters are as follows three
Gbmgrid = Expand.grid (. interaction.depth = C (1, 3),. N.trees = C (d, B, D, D, D, +),. Shrinkage = 0.1) using the train function Training, using the modeling method to enhance the decision tree method,
GbmFit1 = Train (Trainx,trainy,method = "GBM", Trcontrol = Fitcontrol,tunegrid = Gbmgrid,verbose = FALSE) From the result can be observed interacti On.depth the highest accuracy when taking 1,n.trees 150

Interaction.depth n.trees accuracy kappa accuracy SD Kappa SD
1 50 0.822 0.635 0.0577 0.118
1 100 0.824 0.639 0.0574 0.118
1 150 0.826 0.643 0.0635 0.131
1 200 0.824 0.64 0.0605 0.123
1 250 0.816 0.623 0.0608 0.124
1 300 0.824 0.64 0.0584 0.119
3 50 0.816 0.621 0.0569 0.117
3 100 0.82 0.631 0.0578 0.117
3 150 0.815 0.621 0.0582 0.117
3 200 0.82 0.63 0.0618 0.125
3 250 0.813 0.617 0.0632 0.127
3 300 0.812 0.615 0.0622 0.126

The same graphical observation
Plot (GBMFIT1)