Score matrix for multinomial logit model log-likelihood
Parameters : | params : array
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Returns : | The 2-d score vector of the multinomial logit model evaluated at : `params`. : |
Notes
\frac{\partial\ln L}{\partial\beta_{j}}=\sum_{i}\left(d_{ij}-\frac{\exp\left(\beta_{j}^{\prime}x_{i}\right)}{\sum_{k=0}^{J}\exp\left(\beta_{k}^{\prime}x_{i}\right)}\right)x_{i}
for j=1,...,J
In the multinomial model ths score matrix is K x J-1 but is returned as a flattened array to work with the solvers.