Generating informed priors for hyperparameter selection

A system for generating target hyperparameter values of a target machine learning model, the system comprising:

• one or more hardware processors;
• an untuned model evaluator executable by the one or more hardware processors and being configured to iteratively evaluate the target machine learning model using evaluation hyperparameter values of the target machine learning model to measure performance of the target machine learning model for different combinations of the evaluation hyperparameter values,
• a surrogate model trainer executable by the one or more hardware processors and being configured to train a surrogate machine learning model using the different combinations of the evaluation hyperparameter values as features and the performance of the target machine learning model based on a corresponding combination of the evaluation hyperparameter values as labels;
• a feature importance extractor executable by the one or more hardware processors and being configured to generate a feature importance vector of the surrogate machine learning model based on the training of the surrogate machine learning model;
• a probability distribution parameterizer executable by the one or more hardware processors and being configured to generate informed priors based on the feature importance vector;
• and
• a hyperparameter tuner executable by the one or more hardware processors and being configured to generate the target hyperparameter values of the target machine learning model based on the informed priors using Bayesian optimization.

One or more tangible processor-readable storage media embodied with instructions for executing on one or more processors and circuits of a computing device a process for generating target hyperparameter values of a target machine learning model, the process comprising:

• iteratively evaluating the target machine learning model using evaluation hyperparameter values of the target machine learning model to measure performance of the target machine learning model for different combinations of the evaluation hyperparameter values,
• training a surrogate machine learning model using the different combinations of the evaluation hyperparameter values as features and the performance of the target machine learning model based on a corresponding combination of the evaluation hyperparameter values as labels;
• generating a feature importance vector of the surrogate machine learning model based on the training of the surrogate machine learning model;
• generating informed priors based on the feature importance vector;
• and
• generating the target hyperparameter values of the target machine learning model based on the informed priors using Bayesian optimization.