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 | Function | Categorical | Continuous |
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 |