HS1 Fisher

Scoring HS1 is a part of the Fisher Scoring algorithm, which is an iterative optimization technique used to fit a model to data. It is a two-step process that starts with the data being partitioned into two sets: the “training” or “in-sample” set, and the “test” or “out-of-sample” set. In the first step (HS1), the algorithm estimates the parameters of the model for the in-sample data. This involves minimizing the sum of squared residuals between the predicted and observed outcomes. In the second step (HS2), the estimated parameters are used to generate predictions for the data in the test set. The model is then refined by comparing the test set predictions to their corresponding outcomes, and repeating this process until the model has been optimized.