Publications by T. Moudiki
Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
In #151, I introduced a minimal unified interface to XGBoost, CatBoost, LightGBM, and GradientBoosting in Python and R. These models can be automatically calibrated by using GPopt (a package for Bayesian optimization) under the hood. In this post, I’ll show how to obtain prediction sets (classification) and prediction intervals (regression) for t...
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Quick/automated R package development workflow (assuming you’re using macOS or Linux) Part2
Disclaimer: I have no affiliation with the VS Code team, just a user who likes the product. Earlier this week in #155, I posted about a quick/automated workflow for R package development at the command line. Using this workflow along with VS Code Editor – after experimenting it myself – is a breeze… Interested in using VS Code for your R pack...
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R package development workflow (assuming you’re using macOS or Linux)
I’m using VS Code on macOS for Python, R, Javascript (etc.) development. I needed a quick/automated workflow for my R package development at the command line, so I created this Makefile (if necessary install Python): https://gist.github.com/thierrymoudiki/3bd7cfa099aef0c64eb5f91138d8cedb All you need to do is: store the Makefile at the root of...
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A new method for deriving a nonparametric confidence interval for the mean
Last week, I was looking for a way to construct nonparametric confidence intervals for average effects in learningmachine (using Student-T tests to construct confidence intervals for now). So, I thought of one and implemented it. The methodology combines stratified sampling with the generation of pseudo-observations derived from standardized resid...
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Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
The model presented here is a frequentist – conformalized – version of the Bayesian one presented last week in #152. The model is implemented in learningmachine, both in Python and R. Model explanations are given as sensitivity analyses.0 – install packagesFor Rutils::install.packages(c("rmarkdown", "reticulate", "remotes")) Installing packag...
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Auto XGBoost, Auto LighGBM, Auto CatBoost, Auto GradientBoosting
I’ve always wanted to have a minimal unified interface to XGBoost, CatBoost, LightGBM and sklearn's GradientBoosting, without worrying about the different parameters names aliases. So, I had a lot of fun creating unifiedbooster (which is not part of Techtonique, but is a personal swiss knife tool, under the MIT License).In unifiedbooster, there ...
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Copulas for uncertainty quantification in time series forecasting
On Friday (2024-07-26), I presented nnetsauce (“Probabilistic Forecasting with nnetsauce (using Density Estimation, Bayesian inference, Conformal prediction and Vine copulas)”) version 0.23.0 at an sktime (a unified interface for machine learning with time series) meetup. The news for 0.23.0 are:A method cross_val_score: time series cross-valid...
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Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
This post was firstly submitted to the Applied Quantitative Investment Management group on LinkedIn. It illustrates a recipe implemented in Python package nnetsauce for time series forecasting uncertainty quantification (through simulation): sequential split conformal prediction + block bootstrapUnderlying algorithm:Split data into training set, ca...
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learningmachine for Python (new version)
Last week, I (re)introduced learningmachine, an R package for Machine Learning that includes uncertainty quantification for regression and classification, and explainability through sensitivity analysis. This week, I talk about learningmachine for Python. The Python version is a port of the R package, which means:It’s faster to install if R is al...
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My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
Here are thes slides of my presentation at the International Symposium on Forecasting today:https://www.researchgate.net/publication/381957724_Probabilistic_Forecasting_with_RandomizedQuasi-Randomized_networks_presentation_at_the_International_Symposium_on_Forecasting_2024I discussed probabilistic Forecasting with quasi-randomized networks, sequent...
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