Optimize hyperparameters and save results automatically through machine learning algorithm

Optimize hyperparameters and save results automatically through machine learning algorithm

Optimize hyperparameters and save results automatically through machine learning algorithms

HyperparameterHunter

Automatically save and learn from Experiment results, leading to long-term, persistent optimization that remembers all your tests.

HyperparameterHunter provides a wrapper for machine learning algorithms that saves all the important data. Simplify the experimentation and hyperparameter tuning process by letting HyperparameterHunter do the hard work of recording, organizing, and learning from your tests — all while using the same libraries you already do. Don't let any of your experiments go to waste, and start doing hyperparameter optimization the way it was meant to be.

Features

  • Automatically record Experiment results
  • Truly informed hyperparameter optimization that automatically uses past Experiments
  • Eliminate boilerplate code for cross-validation loops, predicting, and scoring
  • Stop worrying about keeping track of hyperparameters, scores, or re-running the same Experiments
  • Use the libraries and utilities you already love

How to Use HyperparameterHunter

Don’t think of HyperparameterHunter as another optimization library that you bring out only when its time to do hyperparameter optimization. Of course, it does optimization, but its better to view HyperparameterHunter as your own personal machine learning toolbox/assistant.

The idea is to start using HyperparameterHunter immediately. Run all of your benchmark/one-off experiments through it.

The more you use HyperparameterHunter, the better your results will be. If you just use it for optimization, sure, it’ll do what you want, but that’s missing the point of HyperparameterHunter.

If you’ve been using it for experimentation and optimization along the entire course of your project, then when you decide to do hyperparameter optimization, HyperparameterHunter is already aware of all that you’ve done, and that’s when HyperparameterHunter does something remarkable. It doesn’t start optimization from scratch like other libraries. It starts from all of the Experiments and previous optimization rounds you’ve already run through it.

machine learning algorithms

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