y |
vector . Real observations.
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experts |
matrix . Matrix of experts previsions.
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eta |
numeric . Regularization parameter.
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fun_reg |
function (NULL). Regularization function to be applied during the optimization.
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fun_reg_grad |
function (NULL). Gradient of the regularization function (to speed up the computations).
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constr_eq |
function (NULL). Constraints (equalities) to be applied during the optimization.
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constr_eq_jac |
function (NULL). Jacobian of the equality constraints (to speed up the computations).
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constr_ineq |
function (NULL). Constraints (inequalities) to be applied during the optimization (... > 0).
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constr_ineq_jac |
function (NULL). Jacobian of the inequality constraints (to speed up the computations).
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loss.type |
character, list or function ("square").
- character
Name of the loss to be applied ('square', 'absolute', 'percentage', or 'pinball');
- list
List with field name equal to the loss name. If using pinball loss, field tau equal to the required quantile in [0,1];
- function
A custom loss as a function of two parameters (prediction, label).
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loss.gradient |
boolean, function (TRUE).
- boolean
If TRUE, the aggregation rule will not be directly applied to the loss function at hand,
but to a gradient version of it. The aggregation rule is then similar to gradient descent aggregation rule.
- function
If loss.type is a function, the derivative of the loss in its first component should be provided to be used (it is not automatically
computed).
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w0 |
numeric (NULL). Vector of initialization for the weights.
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max_iter |
integer (50). Maximum number of iterations of the optimization algorithm per round.
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obj_tol |
numeric (1e-2). Tolerance over objective function between two iterations of the optimization.
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training |
list (NULL). List of previous parameters.
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default |
boolean (FALSE). Whether or not to use default parameters for fun_reg, constr_eq, constr_ineq and their grad/jac,
which values are ALL ignored when TRUE.
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quiet |
boolean (FALSE). Whether or not to display progress bars.
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