This learner uses svm
from e1071
to fit a support
vector machine (SVM).
Lrnr_svm
R6Class
object.
Learner object with methods for training and prediction. See
Lrnr_base
for documentation on learners.
scale = TRUE
A logical vector indicating the variables to be
scaled. For a detailed description, please consult the documentation for
svm
.
type = NULL
SVMs can be used as a classification machine, as
a regression machine, or for novelty detection. Depending of whether the
outcome is a factor or not, the default setting for this argument is
"C-classification" or "eps-regression", respectively. This may be
overwritten by setting an explicit value. For a full set of options,
please consult the documentation for svm
.
kernel = "radial"
The kernel used in training and predicting.
You might consider changing some of the optional parameters, depending on
the kernel type. Options for kernels include: "linear", "polynomial",
"radial" (the default), "sigmoid". For a detailed description, please
consult the documentation for svm
.
fitted = TRUE
Logical indicating whether the fitted values
should be computed and included in the model fit object or not
(default: TRUE
).
probability = FALSE
Logical indicating whether the model
should allow for probability predictions (default: FALSE
).
...
Other parameters passed to svm
.
See its documentation for details.
Other Learners: Custom_chain
,
Lrnr_HarmonicReg
, Lrnr_arima
,
Lrnr_bartMachine
, Lrnr_base
,
Lrnr_bilstm
, Lrnr_condensier
,
Lrnr_cv
,
Lrnr_define_interactions
,
Lrnr_expSmooth
,
Lrnr_glm_fast
, Lrnr_glmnet
,
Lrnr_glm
, Lrnr_h2o_grid
,
Lrnr_hal9001
,
Lrnr_independent_binomial
,
Lrnr_lstm
, Lrnr_mean
,
Lrnr_nnls
, Lrnr_optim
,
Lrnr_pca
,
Lrnr_pkg_SuperLearner
,
Lrnr_randomForest
,
Lrnr_ranger
, Lrnr_rpart
,
Lrnr_rugarch
, Lrnr_sl
,
Lrnr_solnp_density
,
Lrnr_solnp
,
Lrnr_subset_covariates
,
Lrnr_tsDyn
, Lrnr_xgboost
,
Pipeline
, Stack
,
define_h2o_X
,
undocumented_learner