IterativeReweightedLeastSquaresTModel Properties

Gets or sets a value indicating whether standard errors should be computed in the next iteration.

Gets the current iteration number.

Gets the Gradient vector computed in the last Newton-Raphson iteration.

Gets or sets whether the algorithm has converged.

Gets the Hessian matrix computed in the last Newton-Raphson iteration.

Please use MaxIterations instead.

Gets or sets the maximum number of iterations performed by the learning algorithm.

Gets or sets the regression model being learned.

Gets the total number of parameters in the model.

Gets the previous values for the coefficients which were in place before the last learning iteration was performed.

Gets or sets the regularization value to be added in the objective function. Default is 1e-10.

Gets the current values for the coefficients.

Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.

Gets or sets the tolerance value used to determine whether the algorithm has converged.

Gets the last parameter updates in the last iteration.