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A nice feature would be adding a weights vector to the data vector, in order to be able to assign a different "importance for each observation".
Using this suggested feature, it would also be easy to implement a regularisation, by adding another observation for each pair (in the pairwise comparison models), and give it a small weight.
Currently when the directed graph is acyclic, the ML is that the root will basically have an infinite strength, and regularisation fixes that.
The text was updated successfully, but these errors were encountered:
Note that most functions have a parameter alpha that controls regularization. it is off (i.e., equal to 0.0) by default in most functions, but if you set it to some positive value the infinite strength problem should be resolved.
A nice feature would be adding a weights vector to the data vector, in order to be able to assign a different "importance for each observation".
Using this suggested feature, it would also be easy to implement a regularisation, by adding another observation for each pair (in the pairwise comparison models), and give it a small weight.
Currently when the directed graph is acyclic, the ML is that the root will basically have an infinite strength, and regularisation fixes that.
The text was updated successfully, but these errors were encountered: