axe-testcafe
Spearmint
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| axe-testcafe | Spearmint | |
|---|---|---|
| 1 | 2 | |
| 36 | 1,529 | |
| - | 0.1% | |
| 0.0 | 0.0 | |
| almost 4 years ago | over 4 years ago | |
| JavaScript | Python | |
| - | GNU General Public License v3.0 or later |
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axe-testcafe
Spearmint
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Why do tree-based models still outperform deep learning on tabular data?
It occurs to me that a system, trained on peer-reviewed applied-machine-learning literature and Kaggle winners, that generates candidates for structured feature-engineering specifications, based on plaintext descriptions of columns' real-world meaning, should be considered a requisite part of the "meta" here.
Ah, and then you could iterate within the resulting feature-engineering-suggestion space as a hyper-parameter between experiments, which could be optimized with e.g. https://github.com/HIPS/Spearmint . The papers write themselves!
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[D] What kind of Hyperparameter Optimisation do you use?
This was some time ago but I had some promising results with Bayesian optimization using a Gaussian Process prior. The method was developed by the guys who wrote Spearmint. That library doesn't support parallelization but I implemented the same technique in Scala without too much difficulty.
What are some alternatives?
Spearmint - Testing, simplified. || An inclusive, accessibility-first GUI for generating clean, semantic Javascript tests in only a few clicks of a button.
optuna - A hyperparameter optimization framework
axe-core-npm
srbench - A living benchmark framework for symbolic regression
TestCafe - A Node.js tool to automate end-to-end web testing.
yggdrasil-decision-forests - A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
axe-core-maven-html - Tools for using axe for web accessibility testing with JUnit, Selenium, and Playwright
youtube-react - A Youtube clone built in React, Redux, Redux-saga
Cypress - Fast, easy and reliable testing for anything that runs in a browser.
decision-forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
cypress-mailosaur - Mailosaur email and SMS testing commands for Cypress
spaceopt - Hyperparameter optimization via gradient boosting regression