models: Models

Description See Also


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 f m,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
RFSRCModel f m,n S
RFSRCFastModel f m,n S
RPartModel f n S
SurvRegModel S
SurvRegStepAICModel S
SVMModel 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 S
XGBDARTModel f n S
XGBLinearModel f n S
XGBTreeModel f n S

Categorical: b = binary, f = factor, o = ordered
Continuous: m = matrix, n = numeric
Survival: S = Surv

Models may be combined, tuned, or selected with the following meta-model functions.

StackedModel Stacked regression
SuperModel Super learner
SelectedModel Model selection from a candidate set
TunedModel Model tuning over a parameter grid

See Also

modelinfo, fit, resample

MachineShop documentation built on June 18, 2021, 9:06 a.m.