cv_multiple {sparselink} | R Documentation |
Model comparison
Description
Compares predictive methods for multi-task learning (cv_multiple
) or
transfer learning (cv_transfer
) by k
-fold cross-validation.
Usage
cv_multiple(
y,
X,
family,
alpha = 1,
nfolds = 10,
method = c("wrap_separate", "wrap_mgaussian", "sparselink", "wrap_spls"),
alpha.init = 0.95,
type = "exp",
cands = NULL
)
cv_transfer(
y,
X,
family,
alpha = 1,
nfolds = 10,
method = c("wrap_separate", "wrap_glmtrans", "sparselink", "wrap_xrnet"),
alpha.init = 0.95,
type = "exp",
cands = NULL
)
Arguments
y |
|
family |
character |
alpha |
elastic net mixing parameter of final regressions, default: 1 (lasso) |
nfolds |
number of internal cross-validation folds, default: 10 (10-fold cross-validation) |
alpha.init |
elastic net mixing parameter for initial regressions, default: 0.95 (lasso-like elastic net) |
type |
default |
cands |
candidate values for both scaling parameters,
default: |
Value
Returns a list with slots deviance
,
auc
(only relevant if family="binomial"
),
and refit
.
Examples
#--- multi-task learning ---
family <- "gaussian"
data <- sim_data_multi(family=family)
metric <- cv_multiple(y=data$y_train,X=data$X_train,family=family)
metric$deviance
#--- transfer learning ---
family <- "gaussian"
data <- sim_data_trans(family=family)
metric <- cv_transfer(y=data$y_train,X=data$X_train,family=family)
metric$deviance