Why Use HyperparameterHunter?¶
This section provides an overview of the mission and primary uses of HyperparameterHunter, as well as some of its main features.
TL;DR¶
HyperparameterHunter saves your Experiments to provide:
Enhanced, long-term hyperparameter optimization; and
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
Features¶
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