Trains and tests prediction models
Usage
traintest(
y_train,
X_train,
y_test = NULL,
X_test = NULL,
family = "gaussian",
alpha = 1,
method = c("wrap_empty", "wrap_separate", "sparselink"),
alpha.init = 0.95,
type = "exp",
cands = NULL
)Arguments
- y_train
target of training samples: \(n \times q\) matrix (multi-task learning) or list of \(q\) vectors of length \(n_1,\ldots,n_q\) (transfer learning)
- X_train
features of training samples: \(n \times p\) matrix (multi-task learning) or list of \(q\) matrices of dimensions \(n_1 \times p,\ldots,n_q \times p\) (transfer learning)
- y_test
target of testing samples: \(m \times p\) matrix (multi-task learning) or list of \(q\) vectors of length \(m_1,\ldots,m_q\) (transfer learning)
- X_test
features of testing samples: \(m \times p\) matrix (multi-task learning) or list of \(q\) matrices of dimensions \(m_1 \times p,\ldots,m_q \times p\) (transfer learning)
- family
character
"gaussian"or"binomial"- alpha
elastic net mixing parameter of final regressions, default: 1 (lasso)
- alpha.init
elastic net mixing parameter for initial regressions, default: 0.95 (lasso-like elastic net)
- type
default
"exp"scales weights with \(w_{ext}^{v_{ext}}+w_{int}^{v_{int}}\) (see internal function construct_penfacs for details)- cands
candidate values for both scaling parameters, default:
NULL({0, 0.2, 0.4, 0.6, 0.8, 1})
Value
Returns a list with the computation time in slot time, the out-of-sample deviance in slot deviance, the out-of-sample ROC-AUC in slot auc, the coefficients in slot coef, the predicted value in slot y_hat, and the optimal hyperparameters in slot hyperpar.
Examples
#--- multi-task learning ---
# \donttest{
family <- "gaussian"
data <- sim_data_multi(family=family)
result <- traintest(data$y_train,data$X_train,family=family)# }
#> method: wrap_empty
#> method: wrap_separate
#> method: sparselink
#> mode: multi-target learning, alpha.init=0.95 (elastic net), alpha=1 (lasso)
#--- transfer learning ---
# \donttest{
family <- "gaussian"
data <- sim_data_trans(family=family)
result <- traintest(data$y_train,data$X_train,family=family)# }
#> method: wrap_empty
#> method: wrap_separate
#> method: sparselink
#> mode: transfer learning, alpha.init=0.95 (elastic net), alpha=1 (lasso)