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Blogroll | Top

List of posts by date of publication | Top

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