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Blogroll | Top

List of posts by date of publication | Top

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