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Blogroll | Top

List of posts by date of publication | Top

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

Categories | Top

Forecasting

  1. ARIMA-Black-Scholes: 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 out! (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. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor
  2. GPopt for Machine Learning (hyperparameters' tuning)
  3. rtopy: an R to Python bridge -- novelties
  4. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  5. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  6. Overfitting Random Fourier Features: Universal Approximation Property
  7. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce
  8. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  9. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  10. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  11. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  12. RPweave: Unified R + Python + LaTeX System using uv
  13. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  14. Part2 of More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  15. More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  16. Native uncertainty quantification for time series with NGBoost
  17. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  18. Real-time pricing with a pretrained probabilistic stock return model
  19. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  20. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  21. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  22. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  23. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  24. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  25. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  26. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  27. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  28. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  29. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  30. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  31. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  32. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  33. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  34. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  35. Backpropagating quasi-randomized neural networks
  36. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  37. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  38. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  39. Free R, Python and SQL editors in techtonique dot net
  40. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  41. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  42. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  43. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  44. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  45. 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
  46. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  47. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  48. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  49. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  50. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  51. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  52. Survival stacking: survival analysis translated as supervised classification in R and Python
  53. Extending the Theta forecasting method to GLMs and attention
  54. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  55. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  56. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  57. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  58. Web app with DeepSeek R1 and Hugging Face API for chatting
  59. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  60. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  61. Command Line Interface (CLI) for techtonique.net's API
  62. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  63. 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
  64. Techtonique is out! (with a tutorial in various programming languages and formats)
  65. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  66. 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?
  67. 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)
  68. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  69. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  70. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  71. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  72. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  73. GLMNet in Python: Generalized Linear Models
  74. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  75. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  76. Chat with your tabular data in www.techtonique.net
  77. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  78. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  79. R and Python consoles + JupyterLite in www.techtonique.net
  80. Gradient-Boosting anything (alert: high performance)
  81. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  82. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  83. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  84. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  85. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  86. New versions of nnetsauce and ahead
  87. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  88. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  89. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  90. Copulas for uncertainty quantification in time series forecasting
  91. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  92. learningmachine for Python (new version)
  93. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  94. 10 uncertainty quantification methods in nnetsauce forecasting
  95. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  96. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  97. Automated hyperparameter tuning using any conformalized surrogate
  98. Recognizing handwritten digits with Ridge2Classifier
  99. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  100. Probability of receiving a loan; using learningmachine
  101. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  102. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  103. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  104. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  105. learningmachine v1.1.2: for Python
  106. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  107. rtopy (v0.1.1): calling R functions in Python
  108. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  109. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  110. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  111. Tuning Machine Learning models with GPopt's new version Part 2
  112. Tuning Machine Learning models with GPopt's new version
  113. Subsampling continuous and discrete response variables
  114. DeepMTS, a Deep Learning Model for Multivariate Time Series
  115. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  116. 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
  117. Diffusion models in Python with esgtoolkit (Part2)
  118. Diffusion models in Python with esgtoolkit
  119. Quasi-randomized nnetworks in Julia, Python and R
  120. A plethora of datasets at your fingertips
  121. A classifier that's very accurate (and deep)
  122. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  123. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  124. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  125. Version v0.14.0 of nnetsauce for R and Python
  126. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  127. (News from) forecasting in Python with ahead (progress bars and plots)
  128. Forecasting in Python with ahead
  129. Did you ask ChatGPT about who you are?
  130. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  131. Simple interfaces to the forecasting API
  132. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  133. Prediction intervals (not only) for Boosted Configuration Networks in Python
  134. A Machine Learning workflow using Techtonique
  135. Super Mario Bros © in the browser using PyScript
  136. News from ESGtoolkit, ycinterextra, and nnetsauce
  137. Explaining a Keras _neural_ network predictions with the-teller
  138. New version of nnetsauce -- various quasi-randomized networks
  139. A dashboard illustrating bivariate time series forecasting with `ahead`
  140. Forecasting with `ahead` (Python version)
  141. Tuning and interpreting LSBoost
  142. Classification using linear regression
  143. Documentation and source code for GPopt, a package for Bayesian optimization
  144. Hyperparameters tuning with GPopt
  145. A forecasting tool (API) with examples in curl, R, Python
  146. Bayesian Optimization with GPopt Part 2 (save and resume)
  147. Bayesian Optimization with GPopt
  148. Compatibility of nnetsauce and mlsauce with scikit-learn
  149. Explaining xgboost predictions with the teller
  150. An infinity of time series models in nnetsauce
  151. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  152. A deeper learning architecture in nnetsauce
  153. Generalized nonlinear models in nnetsauce
  154. Boosting nonlinear penalized least squares
  155. Technical documentation
  156. A new version of nnetsauce, and a new Techtonique website
  157. Back next week, and a few announcements
  158. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  159. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  160. nnetsauce version 0.5.0, randomized neural networks on GPU
  161. Maximizing your tip as a waiter (Part 2)
  162. Parallel AdaOpt classification
  163. Maximizing your tip as a waiter
  164. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  165. AdaOpt
  166. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  167. Encoding your categorical variables based on the response variable and correlations
  168. Documentation for the querier, a query language for Data Frames
  169. Import data into the querier (now on Pypi), a query language for Data Frames
  170. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  171. A new version of nnetsauce (v0.3.1)
  172. Using R in Python for statistical learning/data science
  173. 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. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling
  2. rtopy: an R to Python bridge -- novelties
  3. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  4. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition
  5. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  6. Overfitting Random Fourier Features: Universal Approximation Property
  7. Counterfactual Scenario Analysis with ahead::ridge2f
  8. ARIMA-Black-Scholes: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  9. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  10. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  11. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  12. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans
  13. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning
  14. unifiedml in R: A Unified Machine Learning Interface
  15. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates)
  16. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  17. RPweave: Unified R + Python + LaTeX System using uv
  18. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  19. Part2 of More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  20. More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  21. R port of llama2.c
  22. Native uncertainty quantification for time series with NGBoost
  23. Real-time pricing with a pretrained probabilistic stock return model
  24. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  25. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  26. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  27. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  28. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  29. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  30. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  31. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  32. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  33. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  34. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  35. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  36. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  37. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  38. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  39. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  40. R version of 'Backpropagating quasi-randomized neural networks'
  41. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  42. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  43. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  44. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  45. Free R, Python and SQL editors in techtonique dot net
  46. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  47. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  48. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  49. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  50. 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
  51. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  52. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  53. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  54. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  55. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  56. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  57. Survival stacking: survival analysis translated as supervised classification in R and Python
  58. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  59. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  60. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  61. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  62. Extending the Theta forecasting method to GLMs and attention
  63. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  64. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  65. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  66. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  67. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  68. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  69. CRAN-like repository for most recent releases of Techtonique's R packages
  70. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  71. Web app with DeepSeek R1 and Hugging Face API for chatting
  72. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  73. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  74. A simple test of the martingale hypothesis in esgtoolkit
  75. Command Line Interface (CLI) for techtonique.net's API
  76. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  77. 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
  78. Techtonique is out! (with a tutorial in various programming languages and formats)
  79. 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?
  80. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  81. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  82. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  83. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  84. GLMNet in Python: Generalized Linear Models
  85. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  86. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  87. Chat with your tabular data in www.techtonique.net
  88. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  89. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  90. R and Python consoles + JupyterLite in www.techtonique.net
  91. Gradient-Boosting anything (alert: high performance): Part2, R version
  92. Gradient-Boosting anything (alert: high performance)
  93. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  94. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  95. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  96. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  97. New versions of nnetsauce and ahead
  98. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  99. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  100. R package development workflow (assuming you're using macOS or Linux)
  101. A new method for deriving a nonparametric confidence interval for the mean
  102. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  103. Bayesian (nonlinear) adaptive learning
  104. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  105. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  106. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  107. Forecasting the Economy
  108. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  109. Probability of receiving a loan; using learningmachine
  110. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  111. Conformalized predictive simulations for univariate time series on more than 250 data sets
  112. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  113. Multiple examples of Machine Learning forecasting with ahead
  114. rtopy (v0.1.1): calling R functions in Python
  115. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  116. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  117. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  118. 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
  119. Diffusion models in Python with esgtoolkit (Part2)
  120. Diffusion models in Python with esgtoolkit
  121. Quasi-randomized nnetworks in Julia, Python and R
  122. A plethora of datasets at your fingertips
  123. A classifier that's very accurate (and deep)
  124. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  125. Version v0.14.0 of nnetsauce for R and Python
  126. A diffusion model: G2++
  127. Diffusion models in ESGtoolkit + announcements
  128. Risk-neutralize simulations
  129. Comparing cross-validation results using crossval_ml and boxplots
  130. Did you ask ChatGPT about who you are?
  131. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  132. Simple interfaces to the forecasting API
  133. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  134. Boosted Configuration (neural) Networks Pt. 2
  135. Boosted Configuration (_neural_) Networks for classification
  136. News from ESGtoolkit, ycinterextra, and nnetsauce
  137. New version of nnetsauce -- various quasi-randomized networks
  138. A dashboard illustrating bivariate time series forecasting with `ahead`
  139. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  140. Time series cross-validation using `crossvalidation` (Part 2)
  141. Fast and scalable forecasting with ahead::ridge2f
  142. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  143. Forecasting with `ahead`
  144. `crossvalidation` and random search for calibrating support vector machines
  145. parallel grid search cross-validation using `crossvalidation`
  146. `crossvalidation` on R-universe, plus a classification example
  147. A forecasting tool (API) with examples in curl, R, Python
  148. An infinity of time series models in nnetsauce
  149. New activation functions in mlsauce's LSBoost
  150. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  151. Classify penguins with nnetsauce's MultitaskClassifier
  152. Bayesian forecasting for uni/multivariate time series
  153. Boosting nonlinear penalized least squares
  154. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  155. Submitting R package to CRAN
  156. Simulation of dependent variables in ESGtoolkit
  157. Forecasting lung disease progression
  158. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  159. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  160. nnetsauce version 0.5.0, randomized neural networks on GPU
  161. Maximizing your tip as a waiter (Part 2)
  162. Maximizing your tip as a waiter
  163. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  164. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  165. Custom errors for cross-validation using crossval::crossval_ml
  166. Encoding your categorical variables based on the response variable and correlations
  167. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  168. Grid search cross-validation using crossval
  169. Time series cross-validation using crossval
  170. On model specification, identification, degrees of freedom and regularization
  171. R notebooks for nnetsauce
  172. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  173. Feedback forms for contributing
  174. nnetsauce for R
  175. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  176. Using R in Python for statistical learning/data science
  177. Model calibration with `crossval`
  178. crossval