Publications by T. Moudiki
Forecasting in Excel using Techtonique’s Machine Learning APIs under the hood
I created a basic Python FastAPI app that allows you to interact with Techtonique web app through Excel. More specifically, Visual Basic for Applications (VBA) Excel. All you need to do is download the Excel file VBA-Web.xlsm, visit “Sheet1” and “Sheet2”, and click the buttons. In “Sheet2”, you should see something like this:Keep in min...
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Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
This week, I released Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. As of September 2024, the tool is in its beta phase (subject to crashes) and will remain completely free to use until December 24, 2024. After registering, you will rece...
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Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
nnetsauceThe news are (reminder: the nnetsauce.Lazy*s do automated Machine Learning benchmarking of multiple models):Update LazyDeepMTS: no more LazyMTS class, instead, you can use LazyDeepMTS with n_layers=1Specify forecasting horizon in LazyDeepMTS (see updated docs and examples/lazy_mts_horizon.py)New class ClassicalMTS for classsical models (fo...
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Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
The model presented here is a frequentist – conformalized – version of the Bayesian one presented in #152. It is implemented in learningmachine, both in Python and R, and is updated as new observations arrive, using Polyak averaging. Model explanations are given as sensitivity analyses. 1 – R version %load_ext rpy2.ipython %%R utils::instal...
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Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
The model presented here is a frequentist – conformalized – version of the Bayesian one presented in #152. It is implemented in learningmachine, both in Python and R, and is updated as new observations arrive, using Polyak averaging. Model explanations are given as sensitivity analyses.1 – R version%load_ext rpy2.ipython %%R utils::install.p...
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New versions of nnetsauce and ahead
New versions of nnetsauce and ahead will be released this week. Big releases, notably with more uncertainty quantification methods implemented in both packages. Stay tuned. PS: You can now (well, again) comment on posts on this website. Hope it works better than Disqus. Related To leave a comment for the author, please follow the link and comment...
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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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