Why Use HyperparameterHunter?

This section provides an overview of the mission and primary uses of HyperparameterHunter, as well as some of its main features.


  • HyperparameterHunter saves your Experiments to provide:

    1. Enhanced, long-term hyperparameter optimization; and

    2. Improved awareness of what you’ve done, what works, and what you should try next

What is HyperparameterHunter?

  • Don’t think of HyperparameterHunter as a new machine learning tool; its a toolbox

    • There are tons of excellent machine learning libraries. The problem is keeping track of them all

    • Impractical to keep track of which libraries work, which hyperparameters are best for whichever algorithms, and how your experiment was set up

    • Let HyperparameterHunter organize your tools for you, while you focus on using the best tool for the job

    • Stop wasting time debating between a screwdriver and a wrench, when you’re staring at a nail

  • Not a new thing to try alongside other algorithms. Its a new way of doing the things you already do

    • Keep using the libraries/algorithms you know and love, just tell HyperparameterHunter about them

  • Provides a simple wrapper for executing machine learning algorithms

    • Automatically saves the testing conditions/hyperparameters, results, predictions, and more

    • Test and evaluate wide range of algorithms from many different libraries in a unified format


  • Stop worrying about keeping track of hyperparameters, scores, or re-running the same Experiments

  • See records of all your Experiments: from birds-eye-view leaderboards, to individual result files

  • Supercharge informed hyperparameter optimization by allowing it to use saved Experiments

    • No need to hold HyperparameterHunter’s hand while it tries to find the Experiment you ran months ago

    • It automatically reads your Experiment files to find the ones that fit, and it learns from them

  • Eliminate boilerplate code for cross-validation loops, predicting, and scoring

  • Have predictions ready to go when its time for ensembling, meta-learning, and finalizing your models