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Blogroll | Top

List of posts by date of publication | Top

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