Publications by T. Moudiki
nnetsauce’s introduction as of 2024-02-11 (new version 0.17.0)
0 – What is nnetsauce?nnetsauce is a general-purpose computational tool for statistical/Machine Learning (ML). As of 2024-02-11, nnetsauce is available for R and Python. It contains various implementations of – shallow and deep – regression, classification, univariate and multivariate probabilistic time series forecasting models.The specifici...
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Tuning Machine Learning models with GPopt’s new version Part 2
A new version of Python package GPopt is available on PyPI. GPopt is a package for stochastic optimization. This type of optimization is particularly useful for tuning machine learning models’ hyperparameters.The main change in GPopt’s v0.4.0 is: the user can name the objective function’s parameters. This makes it easier to use the results di...
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A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
Remark: I have no affiliation with these projects, but I love using quarto and Visual Studio Code’s extension Jupyter To Markdown for these posts. Just wanted to say that.Today:I’ll show you how to use Python’s nnetsauce (version 0.17.0) to download data from the R universe API.I’ll use nnetsauce’s automated Machine Learning (AutoML) to a...
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learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
I created R package learningmachine in the first place, in order to have a unified, object-oriented (using R6), interface for the machine learning algorithms I use the most on tabular data. This (work in progress) package is available on GitHub and the R-universe. There will certainly be a Python version in the future. This post shows how to use le...
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A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
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Diffusion models in Python with esgtoolkit (Part2)
esgtoolkit version v1.1.0Disclaimer: I’ve used Quarto to generate this post, on a (Ubuntu Linux) machine that already had R installed.Install packagepip install esgtoolkit pip install jupyter import numpy as np import pandas as pd import seaborn as sns from esgtoolkit import simdiff, simshocks, esgplotbands from time import time # GBM print(" - G...
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Diffusion models in Python with esgtoolkit
A few weeks ago, on 2023-10-02, I announced esgtoolkit v1.0.0 for Python. Well, v1.0.0 for Python is more like a proof of concept as of today, as there isn’t an exact mapping with the R API so far. Next week, most likely in a v1.1.0 for both, the Python version will be aligned with the R version – as much as possible. For those who aren’t fam...
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Diffusion models in Python with esgtoolkit
A few weeks ago, on 2023-10-02, I announced esgtoolkit v1.0.0 for Python.Well, v1.0.0 for Python is more like a proof of concept as of today, as there isn’t an exact mapping with the R API so far. Next week, most likely in a v1.1.0 for both, the Python version will be aligned with the R version – as much as possible.For those who aren’t famil...
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Quasi-randomized nnetworks in Julia, Python and R
nnetsauce, a package for quasi-randomized supervised learning (classification and regression), is currently available for R and Python. For more details on nnetsauce, you can read these posts.I’ve always wanted to port nnetsauce to the Julia language. However, in the past few years, there was a little timing overhead (more precisely, a lag) when ...
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Diffusion models in ESGtoolkit + announcements
Contents 1 – news 2 – examples 1 – news Version 0.6.2 of ESGtoolkit is now available: on GitHub on R-universe It is now possible to use Hermite cubic spline and Smith-Wilson methods for yield curve interpolation. Starting with version 1.0.0, the next version, ESGtoolkit will be renamed as esgtoolkit or something similar, as I’m removin...
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