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Blogroll | Top

List of posts by date of publication | Top

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

Categories | Top

Forecasting

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

Misc

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

Python

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