Hyperparameter Optimizer is a feature in the impressions modelling that searches for the best combination of model settings in order to achieve the best performance for each customer. Imagine you're a chef trying to create a new recipe. You have several ingredients (like sugar, chocolate, event the amount of cooking time) and you're not sure about the exact amount of each ingredient to use to make the dish taste delicious.
In machine learning, the "recipe" is your model, and the "ingredients" are the hyperparameters. Just like in cooking, the exact amount of each hyperparameter can make a big difference in how well your model performs.
A hyperparameter optimizer is like a kitchen assistant who helps you find the best combination of ingredients - instead of you having to taste-test every possible combination, the assistant uses clever techniques to quickly figure out which combinations are likely to taste the best.
Here's how the optimizer does it:
Sampling: It starts by trying out a few random combinations of hyperparameters
Evaluation: For each combination, it checks how well the model performs (like tasting the dish)
Learning: Based on the results, it learns which combinations are promising and which ones aren't
Iteration: It then tries out new combinations, focusing more on the promising ones
This process continues until the optimizer finds a combination that gives the best performance, or until it has tried out a certain number of combinations.
In essence, a hyperparameter optimizer automates the trial-and-error process of finding the best settings for a machine learning model, and it repeats this regularly, to make sure the models are more reactive to your changes in marketing strategy.