Description Usage Arguments Value Examples
Perform the k-fold cross-validation to estimate the λ_1.
1 2 3 4 |
X |
A set of feature matrices |
Y |
A set of responses, could be binary (classification problem) or continues (regression problem). The valid value of binary outcome \in\{1, -1\} |
type |
The type of problem, must be |
Regularization |
The type of MTL algorithm (cross-task regularizer). The value must be
one of { |
Lam1_seq |
A positive sequence of |
Lam2 |
A positive constant λ_{2} to improve the generalization performance |
G |
A matrix to encode the network information. This parameter
is only used in the MTL with graph structure ( |
k |
A positive number to modulate the structure of clusters
with the default of 2. This parameter is only used in MTL with
clustering structure ( |
opts |
Options of the optimization procedure. One can set the
initial search point, the tolerance and the maximized number of
iterations through the parameter. The default value is
|
stratify |
|
nfolds |
The number of folds |
ncores |
The number of cores used for parallel computing with the default value of 2 |
parallel |
|
The estimated λ_1 and related information
1 2 3 4 5 6 7 8 9 10 | #create the example data
data<-Create_simulated_data(Regularization="L21", type="Classification")
#perform the cross validation
cvfit<-cvMTL(data$X, data$Y, type="Classification", Regularization="L21",
Lam2=0, opts=list(init=0, tol=10^-6, maxIter=1500), nfolds=5,
stratify=TRUE, Lam1_seq=10^seq(1,-4, -1))
#show meta-infomration
str(cvfit)
#plot the CV accuracies across lam1 sequence
plot(cvfit)
|
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