Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
https://thierrymoudiki.github.io/blog/2026/05/21/r/python/Conformalized-TabICL-nnetsauce
@teeeeeeeee.bsky.social
@teeeeeeeee.bsky.socialConformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
https://thierrymoudiki.github.io/blog/2026/05/21/r/python/Conformalized-TabICL-nnetsauce
@teeeeeeeee.bsky.socialA plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
https://thierrymoudiki.github.io/blog/2024/02/19/python/quasirandomizednn/explainableml/nnetsauce-dl-data
@teeeeeeeee.bsky.socialNative uncertainty quantification for time series with NGBoost
https://thierrymoudiki.github.io/blog/2025/10/08/r/python/ngboost-time-series
@teeeeeeeee.bsky.socialAn Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
https://thierrymoudiki.github.io/blog/2025/06/14/python/xgboost-default-overfitting
@teeeeeeeee.bsky.socialGenerating Synthetic Data with R-vine Copulas using esgtoolkit in R
https://thierrymoudiki.github.io/blog/2025/09/21/r/synthetic-copulas
@teeeeeeeee.bsky.socialnnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
https://thierrymoudiki.github.io/blog/2024/02/11/python/quasirandomizednn/nnetsauce-tuto
@teeeeeeeee.bsky.socialBoosted Configuration (neural) Networks Pt. 2
https://thierrymoudiki.github.io/blog/2022/09/03/r/boosted-configuration-networks-pt2
@teeeeeeeee.bsky.socialPrediction intervals for nnetsauce models
https://thierrymoudiki.github.io/blog/2019/10/18/quasirandomizednn/nnetsauce-prediction-intervals
@teeeeeeeee.bsky.socialrtopy (v0.1.1): calling R functions in Python
https://thierrymoudiki.github.io/blog/2024/03/04/python/r/rtopyintro
@teeeeeeeee.bsky.socialForecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition
https://thierrymoudiki.github.io/blog/2026/01/01/r/theta-vs-dynrmf-on-M1-M3-Tourism
@teeeeeeeee.bsky.socialA Machine Learning workflow using Techtonique
https://thierrymoudiki.github.io/blog/2022/06/06/python/lsboost/explainableml/mlsauce/techtonique-workflow
@teeeeeeeee.bsky.socialStock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
https://thierrymoudiki.github.io/blog/2024/12/29/r/stock-forecasting
@teeeeeeeee.bsky.socialAnalyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
https://thierrymoudiki.github.io/blog/2025/12/03/r/python/misc/analyzing-reviews
@teeeeeeeee.bsky.socialConformalized predictive simulations for univariate time series on more than 250 data sets
https://thierrymoudiki.github.io/blog/2024/04/07/r/conformal-time-series
@teeeeeeeee.bsky.social
@teeeeeeeee.bsky.socialTransfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
https://thierrymoudiki.github.io/blog/2025/09/09/r/python/pretraining-ridge2f-part2
@teeeeeeeee.bsky.socialmlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
https://thierrymoudiki.github.io/blog/2025/07/10/python/mlsauce
@teeeeeeeee.bsky.social`crossvalidation` on R-universe, plus a classification example
https://thierrymoudiki.github.io/blog/2021/07/23/r/crossvalidation-r-universe
@teeeeeeeee.bsky.socialExtending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
https://thierrymoudiki.github.io/blog/2025/04/14/r/theta-M1-M3-M4
@teeeeeeeee.bsky.socialLSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
https://thierrymoudiki.github.io/blog/2020/07/24/python/r/lsboost/explainableml/mlsauce/xai-boosting
