ml-specialty video for a pharmaceutical researcher is developing a machine learning model to predict the effectiveness of various drug formulations based on
A pharmaceutical researcher is developing a machine learning model to predict the effectiveness of various drug formulations based on chemical compound data. The model needs to be trained with various hyperparameters such as the learning rate, regularization strength, and batch size, to find the most accurate predictions. The researcher has a large dataset and wants to experiment with different hyperparameter configurations in parallel to identify the best ones. Additionally, the researcher needs to automatically stop configurations that are not improving accuracy early, while allocating more computational resources to promising configurations to complete faster. Which hyperparameter tuning technique should the researcher use to achieve these goals with the LEAST computational time?