The model convergence can be sensitive to the initial weights of coefficients. We added a weight_initialization keyword argument to the ConditionalLogitModel class and {nest, item}_weight_initialization keyword arguments to the NestedLogitModel class. These arguments allow users to control the initialization of coefficients (e.g., initialize to zeros, uniform random, or Gaussian random). Please see the notebook here: ./tutorial/coefficient_initialization.ipynb for demonstrations.
The model convergence can be sensitive to the initial weights of coefficients. We added a
weight_initializationkeyword argument to theConditionalLogitModelclass and{nest, item}_weight_initializationkeyword arguments to theNestedLogitModelclass. These arguments allow users to control the initialization of coefficients (e.g., initialize to zeros, uniform random, or Gaussian random). Please see the notebook here:./tutorial/coefficient_initialization.ipynbfor demonstrations.