tsensembler (version 0.1.0)

model_specs-class: 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. See bm_gaussianprocess for the function implementation.

bm_ppr

Projection Pursuit Regression models, from the stats package. See ppr for a complete description and possible parametrization. See bm_ppr for the function implementation.

bm_glm

Generalized Linear Models, from the glmnet package. See glmnet for a complete description and possible parametrization. See bm_glm for the function implementation.

bm_gbm

Generalized Boosted Regression models, from the gbm package. See gbm for a complete description and possible parametrization. See bm_gbm for the function implementation.

bm_randomforest

Random Forest models, from the ranger package. See ranger for a complete description and possible parametrization. See bm_randomforest for the function implementation.

bm_cubist

M5 tree models, from the Cubist package. See cubist for a complete description and possible parametrization. See bm_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. See bm_mars for the function implementation.

bm_svr

Support Vector Regression models, from the kernlab package. See ksvm for a complete description and possible parametrization. See bm_svr for the function implementation.

bm_ffnn

Feedforward Neural Network models, from the nnet package. See nnet for a complete description and possible parametrization. See bm_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. See bm_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

# NOT RUN {
# 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
     )
  )
)

# }