| Title: | Multiple Approximate Kernel Learning (MAKL) |
| Version: | 1.0.1 |
| Description: | R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in <doi:10.1093/bioinformatics/btac241>. The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Imports: | AUC, grplasso |
| Suggests: | rmarkdown, knitr |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2022-07-06 13:56:03 UTC; user |
| Author: | Ayyüce Begüm Bektaş
 [aut, cre],
Mehmet Gönen
[aut] |
| Maintainer: | Ayyüce Begüm Bektaş <ayyucebektas17@ku.edu.tr> |
| Repository: | CRAN |
| Date/Publication: | 2022-07-06 14:10:02 UTC |
Test the Multiple Approximate Kernel Learning (MAKL) Model
Description
Binary classification of the test data, using the MAKL model resulted from makl_train().
Usage
makl_test(X, y, makl_model)
Arguments
X |
test dataset, matrix of size T x d. |
y |
response vector of length T, containing only -1 and 1. |
makl_model |
a list containing the MAKL model returning from makl_train(). |
Value
a list containing the predictions for test instances and the area under the ROC curve (AUROC) values with corresponding number of used kernels for prediction.
Train a Multiple Approximate Kernel Learning (MAKL) Model
Description
Train a MAKL model to be used as an input to makl_test().
Usage
makl_train(
X,
y,
D = 100,
sigma_N = 1000,
CV = 1,
lambda_set = c(0.9, 0.8, 0.7, 0.6),
membership
)
Arguments
X |
training dataset, matrix of size N x d. |
y |
response vector of length N, containing only -1 and 1. |
D |
numeric value related to the number of random features to be used for approximation. |
sigma_N |
numeric value preferably smaller than N, used to calculate sigma to create random features. |
CV |
integer value between 0 and N. If CV is equal to 0 or 1, no cross validation is performed. If CV is greater than or equal to 2, CV is assigned as fold count in the cross validation. |
lambda_set |
a continuous number between 0 and 1, used for regularization. |
membership |
a list of length of number of groups, containing feature memberships to each group. |
Value
a list containing the MAKL model and related parameters to be used in makl_test().