A new version (v0.3.1) of `nnetsauce`

is 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!