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Blogroll | Top

List of posts by date of publication | Top

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