cvMTL | R Documentation |
Perform the k-fold cross-validation to estimate the λ_1.
cvMTL( X, Y, type = "Classification", Regularization = "L21", Lam1_seq = 10^seq(1, -4, -1), Lam2 = 0, G = NULL, k = 2, opts = list(init = 0, tol = 10^-3, maxIter = 1000), stratify = FALSE, nfolds = 5, ncores = 2, parallel = FALSE )
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
#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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