model_specs-class {tsensembler} | R Documentation |
Setup base learning models
Description
This class sets up the base learning models and respective parameters setting to learn the ensemble.
Slots
learner
character vector with the base learners to be trained. Currently available models are:
- bm_gaussianprocess
Gaussian Process models, from the kernlab package. See
gausspr
for a complete description and possible parametrization. Seebm_gaussianprocess
for the function implementation.- bm_ppr
Projection Pursuit Regression models, from the stats package. See
ppr
for a complete description and possible parametrization. Seebm_ppr
for the function implementation.- bm_glm
Generalized Linear Models, from the glmnet package. See
glmnet
for a complete description and possible parametrization. Seebm_glm
for the function implementation.- bm_gbm
Generalized Boosted Regression models, from the gbm package. See
gbm
for a complete description and possible parametrization. Seebm_gbm
for the function implementation.- bm_randomforest
Random Forest models, from the ranger package. See
ranger
for a complete description and possible parametrization. Seebm_randomforest
for the function implementation.- bm_cubist
M5 tree models, from the Cubist package. See
cubist
for a complete description and possible parametrization. Seebm_cubist
for the function implementation.- bm_mars
Multivariate Adaptive Regression Splines models, from the earth package. See
earth
for a complete description and possible parametrization. Seebm_mars
for the function implementation.- bm_svr
Support Vector Regression models, from the kernlab package. See
ksvm
for a complete description and possible parametrization. Seebm_svr
for the function implementation.- bm_ffnn
Feedforward Neural Network models, from the nnet package. See
nnet
for a complete description and possible parametrization. Seebm_ffnn
for the function implementation.- bm_pls_pcr
Partial Least Regression and Principal Component Regression models, from the pls package. See
mvr
for a complete description and possible parametrization. Seebm_pls_pcr
for the function implementation.
learner_pars
a list with parameter setting for the learner. For each model, a inner list should be created with the specified parameters.
Check each implementation to see the possible variations of parameters (also examplified below).
Examples
# A PPR model and a GLM model with default parameters
model_specs(learner = c("bm_ppr", "bm_glm"), learner_pars = NULL)
# A PPR model and a SVR model. The listed parameters are combined
# with a cartesian product.
# With these specifications an ensemble with 6 predictive base
# models will be created. Two PPR models, one with 2 nterms
# and another with 4; and 4 SVR models, combining the kernel
# and C parameters.
specs <- model_specs(
c("bm_ppr", "bm_svr"),
list(bm_ppr = list(nterms = c(2, 4)),
bm_svr = list(kernel = c("vanilladot", "polydot"), C = c(1,5)))
)
# All parameters currently available (parameter values can differ)
model_specs(
learner = c("bm_ppr", "bm_svr", "bm_randomforest",
"bm_gaussianprocess", "bm_cubist", "bm_glm",
"bm_gbm", "bm_pls_pcr", "bm_ffnn", "bm_mars"
),
learner_pars = list(
bm_ppr = list(
nterms = c(2,4),
sm.method = "supsmu"
),
bm_svr = list(
kernel = "rbfdot",
C = c(1,5),
epsilon = .01
),
bm_glm = list(
alpha = c(1, 0)
),
bm_randomforest = list(
num.trees = 500
),
bm_gbm = list(
interaction.depth = 1,
shrinkage = c(.01, .005),
n.trees = c(100)
),
bm_mars = list(
nk = 15,
degree = 3,
thresh = .001
),
bm_ffnn = list(
size = 30,
decay = .01
),
bm_pls_pcr = list(
method = c("kernelpls", "simpls", "cppls")
),
bm_gaussianprocess = list(
kernel = "vanilladot",
tol = .01
),
bm_cubist = list(
committees = 50,
neighbors = 0
)
)
)