models: Models
In MachineShop: Machine Learning Models and Tools
models R Documentation
Models
Description
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
ParsnipModel f m,n S
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
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 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.
ModelSpecification Model specification
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 Sept. 11, 2024, 6:28 p.m.
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| models | R Documentation |
Models
Description
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 | |
ParsnipModel | f | m,n | S |
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 | |
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 | 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.
ModelSpecification | Model specification |
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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