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Blogroll | Top

List of posts by date of publication | Top

  1. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R Jul 13, 2026
  2. Natively Interpretable Boosting Jul 12, 2026
  3. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus Jun 27, 2026
  4. Using scikit-learn models in R easily with the tisthemachinelearner package Jun 21, 2026
  5. No-Code Machine Learning in Excel with the Techtonique API Jun 14, 2026
  6. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3 Jun 7, 2026
  7. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now) May 31, 2026
  8. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R May 21, 2026
  9. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R May 17, 2026
  10. Probabilistic Time Series Cross-Validation with R package crossvalidation May 16, 2026
  11. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0 May 9, 2026
  12. 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 May 2, 2026
  13. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2 Apr 28, 2026
  14. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages Apr 26, 2026
  15. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets Apr 19, 2026
  16. `mlS3` — A Unified S3 Machine Learning Interface in R Apr 12, 2026
  17. One interface, (Almost) Every Classifier: unifiedml v0.2.1 Apr 4, 2026
  18. Techtonique dot net is down until further notice Apr 1, 2026
  19. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors) Mar 29, 2026
  20. Python version of 'Option pricing using time series models as market price of risk Pt.3' Mar 22, 2026
  21. Option pricing using time series models as market price of risk Pt.3 Mar 16, 2026
  22. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios) Mar 8, 2026
  23. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting Mar 1, 2026
  24. nnetsauce with and without jax for GPU acceleration Feb 23, 2026
  25. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters Feb 16, 2026
  26. R version of Python package survivalist, for model-agnostic survival analysis Feb 9, 2026
  27. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026) Feb 4, 2026
  28. Option pricing using time series models as market price of risk Feb 1, 2026
  29. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f) Jan 31, 2026
  30. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor Jan 29, 2026
  31. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling Jan 25, 2026
  32. GPopt for Machine Learning (hyperparameters' tuning) Jan 21, 2026
  33. rtopy: an R to Python bridge -- novelties Jan 8, 2026
  34. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Jan 3, 2026
  35. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition Jan 1, 2026
  36. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce Dec 17, 2025
  37. Overfitting Random Fourier Features: Universal Approximation Property Dec 13, 2025
  38. Counterfactual Scenario Analysis with ahead::ridge2f Dec 11, 2025
  39. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce Dec 10, 2025
  40. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint) Dec 7, 2025
  41. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis) Dec 3, 2025
  42. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn Dec 1, 2025
  43. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv Nov 21, 2025
  44. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans Nov 16, 2025
  45. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning Nov 13, 2025
  46. unifiedml in R: A Unified Machine Learning Interface Nov 5, 2025
  47. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates) Oct 28, 2025
  48. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation Oct 27, 2025
  49. RPweave: Unified R + Python + LaTeX System using uv Oct 19, 2025
  50. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator Oct 19, 2025
  51. R port of llama2.c Oct 9, 2025
  52. Native uncertainty quantification for time series with NGBoost Oct 8, 2025
  53. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving Oct 6, 2025
  54. Real-time pricing with a pretrained probabilistic stock return model Oct 1, 2025
  55. Combining any model with GARCH(1,1) for probabilistic stock forecasting Sep 23, 2025
  56. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R Sep 21, 2025
  57. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning Sep 16, 2025
  58. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation Sep 9, 2025
  59. Transfer Learning using ahead::ridge2f on synthetic stocks returns Sep 8, 2025
  60. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London... Sep 4, 2025
  61. external regressors in ahead::dynrmf's interface for Machine learning forecasting Sep 1, 2025
  62. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Aug 20, 2025
  63. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting Jul 26, 2025
  64. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization) Jul 15, 2025
  65. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI. Jul 10, 2025
  66. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics). Jul 8, 2025
  67. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting Jul 7, 2025
  68. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting Jul 6, 2025
  69. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting Jul 5, 2025
  70. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting Jul 4, 2025
  71. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting Jul 3, 2025
  72. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting Jul 2, 2025
  73. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian) Jul 1, 2025
  74. R version of 'Backpropagating quasi-randomized neural networks' Jun 24, 2025
  75. Backpropagating quasi-randomized neural networks Jun 23, 2025
  76. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting Jun 21, 2025
  77. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf Jun 18, 2025
  78. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters Jun 14, 2025
  79. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models Jun 13, 2025
  80. Free R, Python and SQL editors in techtonique dot net Jun 9, 2025
  81. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation Jun 7, 2025
  82. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 6, 2025
  83. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 5, 2025
  84. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 4, 2025
  85. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 3, 2025
  86. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 2, 2025
  87. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line) Jun 1, 2025
  88. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis May 31, 2025
  89. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios May 30, 2025
  90. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose May 29, 2025
  91. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out May 28, 2025
  92. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
  93. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
  94. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
  95. Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
  96. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
  97. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
  98. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
  99. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series) Apr 14, 2025
  100. Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
  101. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
  102. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
  103. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
  104. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
  105. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
  106. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
  107. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
  108. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
  109. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
  110. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
  111. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  112. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  113. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart Feb 10, 2025
  114. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  115. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  116. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  117. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition Jan 20, 2025
  118. Techtonique is released! (with a tutorial in various programming languages and formats) Jan 14, 2025
  119. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  120. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Jan 5, 2025
  121. Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 31, 2024
  122. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  123. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  124. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  125. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  126. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  127. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  128. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  129. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  130. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  131. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  132. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  133. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  134. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  135. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  136. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  137. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  138. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  139. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  140. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  141. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  142. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  143. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  144. New versions of nnetsauce and ahead Sep 9, 2024
  145. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  146. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  147. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  148. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  149. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  150. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  151. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  152. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  153. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  154. learningmachine for Python (new version) Jul 15, 2024
  155. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  156. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  157. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  158. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  159. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  160. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  161. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  162. Forecasting the Economy May 27, 2024
  163. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  164. Probability of receiving a loan; using learningmachine May 12, 2024
  165. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  166. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  167. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  168. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  169. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  170. learningmachine v1.1.2: for Python Apr 1, 2024
  171. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  172. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  173. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  174. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  175. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  176. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  177. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  178. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  179. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  180. Subsampling continuous and discrete response variables Jan 22, 2024
  181. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  182. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  183. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  184. 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 Dec 25, 2023
  185. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  186. Diffusion models in Python with esgtoolkit Dec 11, 2023
  187. Julia packaging at the command line Dec 4, 2023
  188. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  189. A plethora of datasets at your fingertips Nov 20, 2023
  190. A classifier that's very accurate (and deep) Nov 12, 2023
  191. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  192. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  193. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  194. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  195. A diffusion model: G2++ Oct 9, 2023
  196. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  197. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  198. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  199. Forecasting in Python with ahead Sep 11, 2023
  200. Risk-neutralize simulations Sep 4, 2023
  201. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  202. Reminder Apr 30, 2023
  203. Did you ask ChatGPT about who you are? Apr 16, 2023
  204. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  205. Simple interfaces to the forecasting API Nov 23, 2022
  206. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  207. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  208. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  209. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  210. A Machine Learning workflow using Techtonique Jun 6, 2022
  211. Super Mario Bros © in the browser using PyScript May 8, 2022
  212. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  213. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  214. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  215. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  216. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  217. Forecasting with `ahead` (Python version) Dec 13, 2021
  218. Tuning and interpreting LSBoost Nov 15, 2021
  219. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  220. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  221. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  222. Forecasting with `ahead` Oct 15, 2021
  223. Classification using linear regression Sep 26, 2021
  224. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  225. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  226. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  227. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  228. Hyperparameters tuning with GPopt Jun 11, 2021
  229. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  230. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  231. Bayesian Optimization with GPopt Apr 16, 2021
  232. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  233. Explaining xgboost predictions with the teller Mar 12, 2021
  234. An infinity of time series models in nnetsauce Mar 6, 2021
  235. New activation functions in mlsauce's LSBoost Feb 12, 2021
  236. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  237. A deeper learning architecture in nnetsauce Dec 18, 2020
  238. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  239. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  240. Generalized nonlinear models in nnetsauce Nov 28, 2020
  241. Boosting nonlinear penalized least squares Nov 21, 2020
  242. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  243. NEWS Oct 30, 2020
  244. A glimpse into my PhD journey Oct 23, 2020
  245. Submitting R package to CRAN Oct 16, 2020
  246. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  247. Forecasting lung disease progression Oct 2, 2020
  248. New nnetsauce Sep 25, 2020
  249. Technical documentation Sep 18, 2020
  250. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  251. Back next week, and a few announcements Sep 4, 2020
  252. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  253. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  254. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  255. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  256. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  257. Announcements Jun 26, 2020
  258. Parallel AdaOpt classification Jun 19, 2020
  259. Comments section and other news Jun 12, 2020
  260. Maximizing your tip as a waiter Jun 5, 2020
  261. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  262. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  263. AdaOpt May 15, 2020
  264. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  265. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  266. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  267. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  268. Grid search cross-validation using crossval Apr 10, 2020
  269. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  270. Time series cross-validation using crossval Mar 27, 2020
  271. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  272. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  273. R notebooks for nnetsauce Mar 6, 2020
  274. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  275. Create a specific feed in your Jekyll blog Feb 21, 2020
  276. Git/Github for contributing to package development Feb 14, 2020
  277. Feedback forms for contributing Feb 7, 2020
  278. nnetsauce for R Jan 31, 2020
  279. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  280. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  281. Search bar, new year 2020 Jan 10, 2020
  282. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  283. Understanding model interactions with the `teller` Dec 13, 2019
  284. Using the `teller` on a classifier Dec 6, 2019
  285. Benchmarking the querier's verbs Nov 29, 2019
  286. Composing the querier's verbs for data wrangling Nov 22, 2019
  287. Comparing and explaining model predictions with the teller Nov 15, 2019
  288. Tests for the significance of marginal effects in the teller Nov 8, 2019
  289. Introducing the teller Nov 1, 2019
  290. Introducing the querier Oct 25, 2019
  291. Prediction intervals for nnetsauce models Oct 18, 2019
  292. Using R in Python for statistical learning/data science Oct 11, 2019
  293. Model calibration with `crossval` Oct 4, 2019
  294. Bagging in the nnetsauce Sep 25, 2019
  295. Adaboost learning with nnetsauce Sep 18, 2019
  296. Change in blog's presentation Sep 4, 2019
  297. nnetsauce on Pypi Jun 5, 2019
  298. More nnetsauce (examples of use) May 9, 2019
  299. nnetsauce Mar 13, 2019
  300. crossval Mar 13, 2019
  301. test Mar 10, 2019

Categories | Top

Forecasting

  1. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  2. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  3. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  4. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  5. Techtonique is released! (with a tutorial in various programming languages and formats)
  6. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  7. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  8. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  9. 10 uncertainty quantification methods in nnetsauce forecasting
  10. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  11. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  12. DeepMTS, a Deep Learning Model for Multivariate Time Series
  13. Version v0.14.0 of nnetsauce for R and Python
  14. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  15. (News from) forecasting in Python with ahead (progress bars and plots)
  16. Forecasting in Python with ahead
  17. Simple interfaces to the forecasting API
  18. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  19. A dashboard illustrating bivariate time series forecasting with `ahead`
  20. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret

Misc

  1. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  2. 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
  3. Julia packaging at the command line
  4. A plethora of datasets at your fingertips
  5. Risk-neutralize simulations
  6. Comparing cross-validation results using crossval_ml and boxplots
  7. Reminder
  8. Did you ask ChatGPT about who you are?
  9. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  10. Boosted Configuration (_neural_) Networks for classification
  11. Super Mario Bros © in the browser using PyScript
  12. News from ESGtoolkit, ycinterextra, and nnetsauce
  13. Time series cross-validation using `crossvalidation` (Part 2)
  14. Fast and scalable forecasting with ahead::ridge2f
  15. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  16. Forecasting with `ahead`
  17. Documentation and source code for GPopt, a package for Bayesian optimization
  18. Hyperparameters tuning with GPopt
  19. A forecasting tool (API) with examples in curl, R, Python
  20. Bayesian Optimization with GPopt Part 2 (save and resume)
  21. Bayesian Optimization with GPopt
  22. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  23. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  24. NEWS
  25. A glimpse into my PhD journey
  26. Forecasting lung disease progression
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. Maximizing your tip as a waiter (Part 2)
  32. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  33. Announcements
  34. Comments section and other news
  35. Maximizing your tip as a waiter
  36. Custom errors for cross-validation using crossval::crossval_ml
  37. Encoding your categorical variables based on the response variable and correlations
  38. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  39. Grid search cross-validation using crossval
  40. Time series cross-validation using crossval
  41. On model specification, identification, degrees of freedom and regularization
  42. Create a specific feed in your Jekyll blog
  43. Git/Github for contributing to package development
  44. Feedback forms for contributing
  45. Change in blog's presentation
  46. test

Python

  1. Natively Interpretable Boosting
  2. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  3. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  4. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  5. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  6. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2
  7. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages
  8. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets
  9. Techtonique dot net is down until further notice
  10. Python version of 'Option pricing using time series models as market price of risk Pt.3'
  11. nnetsauce with and without jax for GPU acceleration
  12. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  13. R version of Python package survivalist, for model-agnostic survival analysis
  14. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor
  15. GPopt for Machine Learning (hyperparameters' tuning)
  16. rtopy: an R to Python bridge -- novelties
  17. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  18. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  19. Overfitting Random Fourier Features: Universal Approximation Property
  20. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce
  21. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  22. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  23. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  24. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  25. RPweave: Unified R + Python + LaTeX System using uv
  26. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  27. Native uncertainty quantification for time series with NGBoost
  28. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  29. Real-time pricing with a pretrained probabilistic stock return model
  30. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  31. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  32. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  33. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  34. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  35. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  36. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  37. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  38. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  39. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  40. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  41. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  42. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  43. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  44. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  45. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  46. Backpropagating quasi-randomized neural networks
  47. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  48. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  49. Free R, Python and SQL editors in techtonique dot net
  50. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  51. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  52. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  53. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  54. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  55. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis
  56. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  57. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  58. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  59. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  60. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  61. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  62. Survival stacking: survival analysis translated as supervised classification in R and Python
  63. Extending the Theta forecasting method to GLMs and attention
  64. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  65. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  66. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  67. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  68. Web app with DeepSeek R1 and Hugging Face API for chatting
  69. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  70. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  71. Command Line Interface (CLI) for techtonique.net's API
  72. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  73. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  74. Techtonique is released! (with a tutorial in various programming languages and formats)
  75. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  76. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  77. Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  78. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  79. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  80. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  81. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  82. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  83. GLMNet in Python: Generalized Linear Models
  84. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  85. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  86. Chat with your tabular data in www.techtonique.net
  87. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  88. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  89. R and Python consoles + JupyterLite in www.techtonique.net
  90. Gradient-Boosting anything (alert: high performance)
  91. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  92. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  93. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  94. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  95. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  96. New versions of nnetsauce and ahead
  97. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  98. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  99. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  100. Copulas for uncertainty quantification in time series forecasting
  101. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  102. learningmachine for Python (new version)
  103. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  104. 10 uncertainty quantification methods in nnetsauce forecasting
  105. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  106. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  107. Automated hyperparameter tuning using any conformalized surrogate
  108. Recognizing handwritten digits with Ridge2Classifier
  109. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  110. Probability of receiving a loan; using learningmachine
  111. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  112. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  113. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  114. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  115. learningmachine v1.1.2: for Python
  116. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  117. rtopy (v0.1.1): calling R functions in Python
  118. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  119. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  120. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  121. Tuning Machine Learning models with GPopt's new version Part 2
  122. Tuning Machine Learning models with GPopt's new version
  123. Subsampling continuous and discrete response variables
  124. DeepMTS, a Deep Learning Model for Multivariate Time Series
  125. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  126. 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
  127. Diffusion models in Python with esgtoolkit (Part2)
  128. Diffusion models in Python with esgtoolkit
  129. Quasi-randomized nnetworks in Julia, Python and R
  130. A plethora of datasets at your fingertips
  131. A classifier that's very accurate (and deep)
  132. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  133. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  134. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  135. Version v0.14.0 of nnetsauce for R and Python
  136. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  137. (News from) forecasting in Python with ahead (progress bars and plots)
  138. Forecasting in Python with ahead
  139. Did you ask ChatGPT about who you are?
  140. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  141. Simple interfaces to the forecasting API
  142. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  143. Prediction intervals (not only) for Boosted Configuration Networks in Python
  144. A Machine Learning workflow using Techtonique
  145. Super Mario Bros © in the browser using PyScript
  146. News from ESGtoolkit, ycinterextra, and nnetsauce
  147. Explaining a Keras _neural_ network predictions with the-teller
  148. New version of nnetsauce -- various quasi-randomized networks
  149. A dashboard illustrating bivariate time series forecasting with `ahead`
  150. Forecasting with `ahead` (Python version)
  151. Tuning and interpreting LSBoost
  152. Classification using linear regression
  153. Documentation and source code for GPopt, a package for Bayesian optimization
  154. Hyperparameters tuning with GPopt
  155. A forecasting tool (API) with examples in curl, R, Python
  156. Bayesian Optimization with GPopt Part 2 (save and resume)
  157. Bayesian Optimization with GPopt
  158. Compatibility of nnetsauce and mlsauce with scikit-learn
  159. Explaining xgboost predictions with the teller
  160. An infinity of time series models in nnetsauce
  161. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  162. A deeper learning architecture in nnetsauce
  163. Generalized nonlinear models in nnetsauce
  164. Boosting nonlinear penalized least squares
  165. Technical documentation
  166. A new version of nnetsauce, and a new Techtonique website
  167. Back next week, and a few announcements
  168. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  169. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  170. nnetsauce version 0.5.0, randomized neural networks on GPU
  171. Maximizing your tip as a waiter (Part 2)
  172. Parallel AdaOpt classification
  173. Maximizing your tip as a waiter
  174. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  175. AdaOpt
  176. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  177. Encoding your categorical variables based on the response variable and correlations
  178. Documentation for the querier, a query language for Data Frames
  179. Import data into the querier (now on Pypi), a query language for Data Frames
  180. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  181. A new version of nnetsauce (v0.3.1)
  182. Using R in Python for statistical learning/data science
  183. nnetsauce on Pypi

QuasiRandomizedNN

  1. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  2. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  3. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  4. 10 uncertainty quantification methods in nnetsauce forecasting
  5. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  6. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  7. Automated hyperparameter tuning using any conformalized surrogate
  8. Recognizing handwritten digits with Ridge2Classifier
  9. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  10. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  11. DeepMTS, a Deep Learning Model for Multivariate Time Series
  12. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  13. Quasi-randomized nnetworks in Julia, Python and R
  14. A classifier that's very accurate (and deep)
  15. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  16. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  17. Version v0.14.0 of nnetsauce for R and Python
  18. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  19. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  20. New version of nnetsauce -- various quasi-randomized networks
  21. Tuning and interpreting LSBoost
  22. Classification using linear regression
  23. An infinity of time series models in nnetsauce
  24. A deeper learning architecture in nnetsauce
  25. Classify penguins with nnetsauce's MultitaskClassifier
  26. Bayesian forecasting for uni/multivariate time series
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. nnetsauce version 0.5.0, randomized neural networks on GPU
  32. R notebooks for nnetsauce
  33. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  34. Feedback forms for contributing
  35. nnetsauce for R
  36. A new version of nnetsauce (v0.3.1)
  37. 2019 Recap, the nnetsauce, the teller and the querier
  38. Prediction intervals for nnetsauce models
  39. Bagging in the nnetsauce
  40. Adaboost learning with nnetsauce
  41. nnetsauce on Pypi
  42. More nnetsauce (examples of use)
  43. nnetsauce

R

  1. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R
  2. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  3. Using scikit-learn models in R easily with the tisthemachinelearner package
  4. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3
  5. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  6. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  7. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  8. Probabilistic Time Series Cross-Validation with R package crossvalidation
  9. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0
  10. 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
  11. `mlS3` — A Unified S3 Machine Learning Interface in R
  12. One interface, (Almost) Every Classifier: unifiedml v0.2.1
  13. Techtonique dot net is down until further notice
  14. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors)
  15. Option pricing using time series models as market price of risk Pt.3
  16. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios)
  17. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting
  18. nnetsauce with and without jax for GPU acceleration
  19. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  20. R version of Python package survivalist, for model-agnostic survival analysis
  21. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026)
  22. Option pricing using time series models as market price of risk
  23. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f)
  24. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling
  25. rtopy: an R to Python bridge -- novelties
  26. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  27. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition
  28. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  29. Overfitting Random Fourier Features: Universal Approximation Property
  30. Counterfactual Scenario Analysis with ahead::ridge2f
  31. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  32. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  33. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  34. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  35. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans
  36. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning
  37. unifiedml in R: A Unified Machine Learning Interface
  38. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates)
  39. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  40. RPweave: Unified R + Python + LaTeX System using uv
  41. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  42. R port of llama2.c
  43. Native uncertainty quantification for time series with NGBoost
  44. Real-time pricing with a pretrained probabilistic stock return model
  45. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  46. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  47. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  48. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  49. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  50. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  51. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  52. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  53. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  54. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  55. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  56. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  57. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  58. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  59. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  60. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  61. R version of 'Backpropagating quasi-randomized neural networks'
  62. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  63. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  64. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  65. Free R, Python and SQL editors in techtonique dot net
  66. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  67. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  68. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  69. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  70. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis
  71. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  72. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  73. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  74. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  75. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  76. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  77. Survival stacking: survival analysis translated as supervised classification in R and Python
  78. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  79. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  80. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  81. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  82. Extending the Theta forecasting method to GLMs and attention
  83. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  84. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  85. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  86. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  87. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  88. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  89. CRAN-like repository for most recent releases of Techtonique's R packages
  90. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  91. Web app with DeepSeek R1 and Hugging Face API for chatting
  92. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  93. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  94. A simple test of the martingale hypothesis in esgtoolkit
  95. Command Line Interface (CLI) for techtonique.net's API
  96. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  97. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  98. Techtonique is released! (with a tutorial in various programming languages and formats)
  99. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  100. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  101. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  102. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  103. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  104. GLMNet in Python: Generalized Linear Models
  105. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  106. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  107. Chat with your tabular data in www.techtonique.net
  108. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  109. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  110. R and Python consoles + JupyterLite in www.techtonique.net
  111. Gradient-Boosting anything (alert: high performance): Part2, R version
  112. Gradient-Boosting anything (alert: high performance)
  113. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  114. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  115. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  116. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  117. New versions of nnetsauce and ahead
  118. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  119. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  120. R package development workflow (assuming you're using macOS or Linux)
  121. A new method for deriving a nonparametric confidence interval for the mean
  122. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  123. Bayesian (nonlinear) adaptive learning
  124. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  125. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  126. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  127. Forecasting the Economy
  128. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  129. Probability of receiving a loan; using learningmachine
  130. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  131. Conformalized predictive simulations for univariate time series on more than 250 data sets
  132. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  133. Multiple examples of Machine Learning forecasting with ahead
  134. rtopy (v0.1.1): calling R functions in Python
  135. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  136. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  137. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  138. 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
  139. Diffusion models in Python with esgtoolkit (Part2)
  140. Diffusion models in Python with esgtoolkit
  141. Quasi-randomized nnetworks in Julia, Python and R
  142. A plethora of datasets at your fingertips
  143. A classifier that's very accurate (and deep)
  144. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  145. Version v0.14.0 of nnetsauce for R and Python
  146. A diffusion model: G2++
  147. Diffusion models in ESGtoolkit + announcements
  148. Risk-neutralize simulations
  149. Comparing cross-validation results using crossval_ml and boxplots
  150. Did you ask ChatGPT about who you are?
  151. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  152. Simple interfaces to the forecasting API
  153. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  154. Boosted Configuration (neural) Networks Pt. 2
  155. Boosted Configuration (_neural_) Networks for classification
  156. News from ESGtoolkit, ycinterextra, and nnetsauce
  157. New version of nnetsauce -- various quasi-randomized networks
  158. A dashboard illustrating bivariate time series forecasting with `ahead`
  159. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  160. Time series cross-validation using `crossvalidation` (Part 2)
  161. Fast and scalable forecasting with ahead::ridge2f
  162. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  163. Forecasting with `ahead`
  164. `crossvalidation` and random search for calibrating support vector machines
  165. parallel grid search cross-validation using `crossvalidation`
  166. `crossvalidation` on R-universe, plus a classification example
  167. A forecasting tool (API) with examples in curl, R, Python
  168. An infinity of time series models in nnetsauce
  169. New activation functions in mlsauce's LSBoost
  170. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  171. Classify penguins with nnetsauce's MultitaskClassifier
  172. Bayesian forecasting for uni/multivariate time series
  173. Boosting nonlinear penalized least squares
  174. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  175. Submitting R package to CRAN
  176. Simulation of dependent variables in ESGtoolkit
  177. Forecasting lung disease progression
  178. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  179. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  180. nnetsauce version 0.5.0, randomized neural networks on GPU
  181. Maximizing your tip as a waiter (Part 2)
  182. Maximizing your tip as a waiter
  183. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  184. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  185. Custom errors for cross-validation using crossval::crossval_ml
  186. Encoding your categorical variables based on the response variable and correlations
  187. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  188. Grid search cross-validation using crossval
  189. Time series cross-validation using crossval
  190. On model specification, identification, degrees of freedom and regularization
  191. R notebooks for nnetsauce
  192. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  193. Feedback forms for contributing
  194. nnetsauce for R
  195. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  196. Using R in Python for statistical learning/data science
  197. Model calibration with `crossval`
  198. crossval