aum_linear_model_ls {aum} | R Documentation |
aum linear model ls
Description
Learn a linear model with weights that minimize AUM. Weights are initialized as a vector of zeros, then optimized using gradient descent with exact line search.
Usage
aum_linear_model_ls(feature.list,
diff.list, max.steps = NULL,
improvement.thresh = NULL,
maxIterations = "min.aum",
initial.weight.fun = NULL)
Arguments
feature.list |
List with named elements subtrain and validation, each should be a scaled feature matrix. |
diff.list |
List with named elements subtrain and validation, each should be a data table of differences in error functions. |
max.steps |
positive integer: max number of steps of gradient descent with
exact line search (specify either this or |
improvement.thresh |
non-negative real number: keep doing gradient descent while the
improvement in objective is greater than this number (specify either
this or |
maxIterations |
max number of iterations of exact line search. If "max.auc" then
the objective for |
initial.weight.fun |
Function for computing initial weights, default NULL means use a random standard normal vector. |
Value
Linear model represented as a list of class aum_linear_model
with
named elements: loss is a data table of values for subtrain and
optionally validation at each step, weight.vec is the final vector
of weights learned via gradient descent, intercept is the value
which results in minimal total error (FP+FN), learned via a linear
scan over all possible values given the final weight vector, and
search is a data table with one row for each step (best step size
and number of iterations of line search).
Author(s)
Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre], Jadon Fowler [aut] (Contributed exact line search C++ code)