Model constructor functions supplied by MachineShop are summarized in the table below according to the types of response variables with which each can be used.
| Function | Categorical | Continuous | Survival |
AdaBagModel |
f | ||
AdaBoostModel |
f | ||
BARTModel |
f | n | S |
BARTMachineModel |
b | n | |
BlackBoostModel |
b | n | S |
C50Model |
f | ||
CForestModel |
f | n | S |
CoxModel |
S | ||
CoxStepAICModel |
S | ||
EarthModel |
f | n | |
FDAModel |
f | ||
GAMBoostModel |
b | n | S |
GBMModel |
f | n | S |
GLMBoostModel |
b | n | S |
GLMModel |
b | n | |
GLMStepAICModel |
b | n | |
GLMNetModel |
f | m,n | S |
KNNModel |
f,o | n | |
LARSModel |
n | ||
LDAModel |
f | ||
LMModel |
f | m,n | |
MDAModel |
f | ||
NaiveBayesModel |
f | ||
NNetModel |
f | n | |
PDAModel |
f | ||
PLSModel |
f | n | |
POLRModel |
o | ||
QDAModel |
f | ||
RandomForestModel |
f | n | |
RangerModel |
f | n | S |
RPartModel |
f | n | S |
SurvRegModel |
S | ||
SurvRegStepAICModel |
S | ||
SVMModel |
f | n | |
SVMANOVAModel |
f | n | |
SVMBesselModel |
f | n | |
SVMLaplaceModel |
f | n | |
SVMLinearModel |
f | n | |
SVMPolyModel |
f | n | |
SVMRadialModel |
f | n | |
SVMSplineModel |
f | n | |
SVMTanhModel |
f | n | |
TreeModel |
f | n | |
XGBModel |
f | n | |
XGBDARTModel |
f | n | |
XGBLinearModel |
f | n | |
XGBTreeModel |
f | n |
Categorical: b = binary, f = factor, o = ordered Continuous: m = matrix, n = numeric Survival: S = Surv
Automated combinations, tuning, or selection of these models can be defined with the following meta-model functions:
StackedModel |
Stacked Regression |
SuperModel |
Super Learner |
TunedModel |
Model Tuning over Parameter Grid |
SelectedModel |
Model Selection from Candidate Set |