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Published on https://computationalmindset.com/en/ the page “Fitting functions with a configurable XGBoost regressor”.

The page deals with the approximation of scalar mathematical functions to one or more real variables using a #xgboost regressor without writing code but only acting on the command line of Python scripts; the goal is to demonstrate that regression with XGBoost achieve low error values with extremely short learning times.

Here the url to the page:
https://computationalmindset.com/en/machine-learning/fitting-with-configurable-xgboost.html

Here is the url to the source code on GitHub:
https://github.com/ettoremessina/function-fitting/tree/master/xgboost/


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Pubblicata sul sito https://computationalmindset.com/it/ la pagina “Approssimazione di funzioni tramite un regressore XGBoost configurabile”.

La pagina tratta dell’approssimazione di funzioni matematiche scalari a una o più variabili reali tramite un regressore #xgboost senza scrivere codice ma agendo solamente sulla linea di comando di script Python; l’obiettivo è di dimostrare che l’approssimazione di funzioni con XGBoost raggiungere bassi valori di errore con tempi di learning estremamente brevi.

Qui l’url alla pagina
https://computationalmindset.com/it/machine-learning/approssimazione-di-funzioni-con-xgboost-configurabile.html

Qui l’url ai sorgenti su GitHub:
https://github.com/ettoremessina/function-fitting/tree/master/xgboost/


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The first post on time series was published on https://computationalmindset.com/en/

The post deals with the forecast of univariate and equally spaced time series using a suite of programs written in Python to perform experiments on this type of series using different neural network taxonomies implemented with TensorFlow 2.x; all this without writing code but only acting on the command line of the programs in the suite itself.

At this link for details:
https://computationalmindset.com/it/reti-neurali/forecast-di-una-serie-temporale-univariata-equispaziata-con-tensorflow.html

The full suite code, freely distributed on GitHub under the MIT license, is available for download at this link:
https://github.com/ettoremessina/time-series-and-neural-networks/tree/master/forecast/univariate-equally-spaced/tensorflow

«From Science comes Prevision; from Prevision comes Action.»
(Auguste Comte)

About

Ettore Messina

I am the author of https://computationalmindset.com/ a website that deals with computation in general and neural networks and quantum programming in particular.