How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3 thierrymoudiki.github.io/blog/2026/06... #rstats

Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now) thierrymoudiki.github.io/blog/2026/05... #python #rstats #techtonique

Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R thierrymoudiki.github.io/blog/2026/05... #rstats #python

Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R thierrymoudiki.github.io/blog/2026/05... #python #rstats

Probabilistic Time Series Cross-Validation with R package "crossvalidation" thierrymoudiki.github.io/blog/2026/05... #rstats

One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0 thierrymoudiki.github.io/blog/2026/05... #rstats

You Don’t Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis thierrymoudiki.github.io/blog/2026/05... #rstats

Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2 thierrymoudiki.github.io/blog/2026/04... #python #survivalanalysis

Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking survivalist vs Established Packages Comparing survivalist to established survival analysis packages (scikit-survival, lifelines) on 15 real-world datasets. thierrymoudiki.github.io/blog/2026/04... #python #survival

Conformal Optimization beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets Link to the notebook at then of the post: thierrymoudiki.github.io/blog/2026/04... #python

Transfer 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 #Techtonique #DataScience #Python #rstats #MachineLearning

Technical documentation https://thierrymoudiki.github.io/blog/2020/09/18/misc/python/quasirandomizednn/databases/mlsauce/explainableml/techtonique-website #Techtonique #DataScience #Python #rstats #MachineLearning

Quasi-randomized nnetworks in Julia, Python and R https://thierrymoudiki.github.io/blog/2023/11/27/python/r/julia/quasirandomizednn/julia-nnetsauce #Techtonique #DataScience #Python #rstats #MachineLearning

Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks https://thierrymoudiki.github.io/blog/2024/06/17/python/quasirandomizednn/forecasting/nnetsauce-airpassengers #Techtonique #DataScience #Python #rstats #MachineLearning

Back next week, and a few announcements https://thierrymoudiki.github.io/blog/2020/09/04/misc/python/quasirandomizednn/nnetsauce #Techtonique #DataScience #Python #rstats #MachineLearning

R package development workflow (assuming you're using macOS or Linux) https://thierrymoudiki.github.io/blog/2024/08/27/r/makefile-r-pkg #Techtonique #DataScience #Python #rstats #MachineLearning

Explaining a Keras _neural_ network predictions with the-teller https://thierrymoudiki.github.io/blog/2022/03/11/python/explainableml/teller-keras-explainer #Techtonique #DataScience #Python #rstats #MachineLearning

A new version of nnetsauce (v0.3.1) https://thierrymoudiki.github.io/blog/2020/01/24/python/quasirandomizednn/nnetsauce-2 #Techtonique #DataScience #Python #rstats #MachineLearning

Bayesian Optimization with GPopt https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt #Techtonique #DataScience #Python #rstats #MachineLearning

Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting https://thierrymoudiki.github.io/blog/2024/09/02/python/r/conformalized-unibooster #Techtonique #DataScience #Python #rstats #MachineLearning

mlS3 — A Unified S3 Machine Learning Interface in R Key Design Principle All models follow the same two-step workflow: mod <- wrap_*(X_train, y_train, ...) # Train pred <- predict(mod, newx = X_test, ...) # Predict thierrymoudiki.github.io/blog/2026/04... #rstats

New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees https://thierrymoudiki.github.io/blog/2020/07/03/misc/mlsauce/mlsauce-new #Techtonique #DataScience #Python #rstats #MachineLearning

(News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction https://thierrymoudiki.github.io/blog/2025/03/09/r/ridge2-conformal #Techtonique #DataScience #Python #rstats #MachineLearning

Forecasting lung disease progression https://thierrymoudiki.github.io/blog/2020/10/02/misc/r/osic-kaggle #Techtonique #DataScience #Python #rstats #MachineLearning

Real-time pricing with a pretrained probabilistic stock return model https://thierrymoudiki.github.io/blog/2025/10/01/r/python/realtime-pricing #Techtonique #DataScience #Python #rstats #MachineLearning

You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting https://thierrymoudiki.github.io/blog/2024/12/01/python/nnetsauce-sktime-LLM-example-Pt2 #Techtonique #DataScience #Python #rstats #MachineLearning

Generalized nonlinear models in nnetsauce https://thierrymoudiki.github.io/blog/2020/11/28/explainableml/python/glms #Techtonique #DataScience #Python #rstats #MachineLearning

How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API https://thierrymoudiki.github.io/blog/2025/05/27/r/python/survival-curves-techtonique-API #Techtonique #DataScience #Python #rstats #MachineLearning

Encoding your categorical variables based on the response variable and correlations https://thierrymoudiki.github.io/blog/2020/04/24/python/r/misc/target-encoder-correlation #Techtonique #DataScience #Python #rstats #MachineLearning

Composing the querier's verbs for data wrangling https://thierrymoudiki.github.io/blog/2019/11/22/databases/the-querier-2 #Techtonique #DataScience #Python #rstats #MachineLearning

Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion https://thierrymoudiki.github.io/blog/2025/03/08/r/python/llms/word-online #Techtonique #DataScience #Python #rstats #MachineLearning

New version of nnetsauce -- various quasi-randomized networks https://thierrymoudiki.github.io/blog/2022/02/12/r/python/quasirandomizednn/new-nnetsauce #Techtonique #DataScience #Python #rstats #MachineLearning

A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. https://thierrymoudiki.github.io/blog/2022/11/02/python/r/forecasting/misc/forecasting-api #Techtonique #DataScience #Python #rstats #MachineLearning

Did you ask ChatGPT about who you are? https://thierrymoudiki.github.io/blog/2023/04/16/python/r/misc/bcn-chatgpt #Techtonique #DataScience #Python #rstats #MachineLearning

Bayesian forecasting for uni/multivariate time series https://thierrymoudiki.github.io/blog/2020/12/04/r/quasirandomizednn/bayesian-forecasting #Techtonique #DataScience #Python #rstats #MachineLearning

One interface, (Almost) Every Classifier: unifiedml v0.2.1 A new version of unifiedml is out; available on CRAN. unifiedml is an (ongoing) effort to offer a unified interface to R’s machine learning models. thierrymoudiki.github.io/blog/2026/04... #rstats

Version v0.14.0 of nnetsauce for R and Python https://thierrymoudiki.github.io/blog/2023/10/16/python/r/forecasting/quasirandomizednn/nnetsauce-mts-R #Techtonique #DataScience #Python #rstats #MachineLearning

DeepMTS, a Deep Learning Model for Multivariate Time Series https://thierrymoudiki.github.io/blog/2024/01/15/python/quasirandomizednn/forecasting/DeepMTS #Techtonique #DataScience #Python #rstats #MachineLearning

DeepMTS, a Deep Learning Model for Multivariate Time Series https://thierrymoudiki.github.io/blog/2024/01/15/python/quasirandomizednn/forecasting/DeepMTS #Techtonique #DataScience #Python #rstats #MachineLearning

RPweave: Unified R + Python + LaTeX System using uv https://thierrymoudiki.github.io/blog/2025/10/19/r/python/RPweave #Techtonique #DataScience #Python #rstats #MachineLearning

Julia packaging at the command line https://thierrymoudiki.github.io/blog/2023/12/04/julia/misc/julia-pkging #Techtonique #DataScience #Python #rstats #MachineLearning

Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) https://thierrymoudiki.github.io/blog/2020/07/31/python/r/lsboost/explainableml/mlsauce/xai-boosting-2 #Techtonique #DataScience #Python #rstats #MachineLearning

New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization) https://thierrymoudiki.github.io/blog/2025/07/15/python/new-nnetsauce #Techtonique #DataScience #Python #rstats #MachineLearning

**IMPORTANT: The website techtonique dot net [https://lnkd.in/eAVJc4rJ](lnkd.in/eAVJc4rJ) is down until further notice.** techtonique dot net contained a language-agnostic API for machine learning tasks (classification, regression, survival analysis, etc.). PS: It's not an April's fool joke.

Understanding Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors) What is sensitivity analysis in this context? It’s about evaluating the impact of changes in the external regressors on the time-series forecast. thierrymoudiki.github.io/blog/2026/03... #rstats

nnetsauce on Pypi https://thierrymoudiki.github.io/blog/2019/06/05/python/quasirandomizednn/pypi-nnetsauce #Techtonique #DataScience #Python #rstats #MachineLearning

Simple interfaces to the forecasting API https://thierrymoudiki.github.io/blog/2022/11/23/python/r/forecasting/forecasting-api-interface #Techtonique #DataScience #Python #rstats #MachineLearning

survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce https://thierrymoudiki.github.io/blog/2024/12/15/python/agnostic-survival-analysis #Techtonique #DataScience #Python #rstats #MachineLearning

Parallel AdaOpt classification https://thierrymoudiki.github.io/blog/2020/06/19/python/adaopt/mlsauce/adaopt-classifier-4 #Techtonique #DataScience #Python #rstats #MachineLearning

AutoML in nnetsauce (randomized and quasi-randomized nnetworks) https://thierrymoudiki.github.io/blog/2023/10/22/python/quasirandomizednn/nnetsauce-lazy-predict-preview #Techtonique #DataScience #Python #rstats #MachineLearning