Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user every month, no matter the pricing tier.
A new version (v0.3.1) of nnetsauceis now available. The stable version on PyPi, and a development version on Github. Notable changes for this new version are:
- The inclusion of an upper bound on the error rate of Adaboost: crucial, because the error rate at each iteration has to be at least as good as random guess’.
- New quasi-randomized networks models for regression and classification, with two shrinkage parameters (for model regularization).
The full list of changes can always be found here on Github and a notebook describing some of the new models (for classification) here for 4 datasets (with a snippet below on a wine classification dataset).
Contributions/remarks are welcome as usual, you can submit a pull request on Github.
Note: I am currently looking for a gig. You can hire me on Malt or send me an email: thierry dot moudiki at pm dot me. I can do descriptive statistics, data preparation, feature engineering, model calibration, training and validation, and model outputs’ interpretation. I am fluent in Python, R, SQL, Microsoft Excel, Visual Basic (among others) and French. My résumé? Here!
Citation
For attribution, please cite this work as:
T. Moudiki (2020-01-24). A new version of nnetsauce (v0.3.1). Retrieved from https://thierrymoudiki.github.io/blog/2020/01/24/python/quasirandomizednn/nnetsauce-2
BibTeX citation (remove empty spaces)
@misc{ tmoudiki20200124,
author = { T. Moudiki },
title = { A new version of nnetsauce (v0.3.1) },
url = { https://thierrymoudiki.github.io/blog/2020/01/24/python/quasirandomizednn/nnetsauce-2 },
year = { 2020 } }
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