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Blogroll | Top

List of posts by date of publication | Top

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