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Blogroll | Top

List of posts by date of publication | Top

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