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Blogroll | Top

List of posts by date of publication | Top

  1. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting Jul 5, 2025
  2. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting Jul 4, 2025
  3. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting Jul 3, 2025
  4. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting Jul 2, 2025
  5. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian) Jul 1, 2025
  6. R version of 'Backpropagating quasi-randomized neural networks' Jun 24, 2025
  7. Backpropagating quasi-randomized neural networks Jun 23, 2025
  8. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting Jun 21, 2025
  9. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf Jun 18, 2025
  10. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters Jun 14, 2025
  11. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models Jun 13, 2025
  12. Exceptionally, and on a more personal note (otherwise I may get buried alive)... Jun 10, 2025
  13. Free R, Python and SQL editors in techtonique dot net Jun 9, 2025
  14. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation Jun 7, 2025
  15. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 6, 2025
  16. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 5, 2025
  17. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 4, 2025
  18. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 3, 2025
  19. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 2, 2025
  20. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line) Jun 1, 2025
  21. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis May 31, 2025
  22. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios May 30, 2025
  23. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose May 29, 2025
  24. 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
  25. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
  26. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
  27. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
  28. Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
  29. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
  30. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
  31. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
  32. 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
  33. Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
  34. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
  35. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
  36. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
  37. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
  38. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
  39. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
  40. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
  41. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
  42. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
  43. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
  44. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  45. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  46. 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
  47. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  48. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  49. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  50. 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
  51. Techtonique is out! (with a tutorial in various programming languages and formats) Jan 14, 2025
  52. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  53. 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
  54. 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
  55. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  56. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  57. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  58. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  59. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  60. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  61. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  62. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  63. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  64. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  65. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  66. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  67. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  68. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  69. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  70. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  71. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  72. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  73. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  74. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  75. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  76. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  77. New versions of nnetsauce and ahead Sep 9, 2024
  78. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  79. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  80. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  81. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  82. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  83. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  84. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  85. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  86. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  87. learningmachine for Python (new version) Jul 15, 2024
  88. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  89. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  90. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  91. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  92. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  93. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  94. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  95. Forecasting the Economy May 27, 2024
  96. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  97. Probability of receiving a loan; using learningmachine May 12, 2024
  98. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  99. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  100. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  101. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  102. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  103. learningmachine v1.1.2: for Python Apr 1, 2024
  104. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  105. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  106. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  107. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  108. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  109. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  110. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  111. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  112. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  113. Subsampling continuous and discrete response variables Jan 22, 2024
  114. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  115. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  116. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  117. 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
  118. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  119. Diffusion models in Python with esgtoolkit Dec 11, 2023
  120. Julia packaging at the command line Dec 4, 2023
  121. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  122. A plethora of datasets at your fingertips Nov 20, 2023
  123. A classifier that's very accurate (and deep) Nov 12, 2023
  124. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  125. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  126. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  127. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  128. A diffusion model: G2++ Oct 9, 2023
  129. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  130. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  131. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  132. Forecasting in Python with ahead Sep 11, 2023
  133. Risk-neutralize simulations Sep 4, 2023
  134. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  135. Reminder Apr 30, 2023
  136. Did you ask ChatGPT about who you are? Apr 16, 2023
  137. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  138. Simple interfaces to the forecasting API Nov 23, 2022
  139. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  140. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  141. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  142. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  143. A Machine Learning workflow using Techtonique Jun 6, 2022
  144. Super Mario Bros © in the browser using PyScript May 8, 2022
  145. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  146. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  147. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  148. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  149. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  150. Forecasting with `ahead` (Python version) Dec 13, 2021
  151. Tuning and interpreting LSBoost Nov 15, 2021
  152. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  153. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  154. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  155. Forecasting with `ahead` Oct 15, 2021
  156. Classification using linear regression Sep 26, 2021
  157. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  158. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  159. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  160. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  161. Hyperparameters tuning with GPopt Jun 11, 2021
  162. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  163. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  164. Bayesian Optimization with GPopt Apr 16, 2021
  165. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  166. Explaining xgboost predictions with the teller Mar 12, 2021
  167. An infinity of time series models in nnetsauce Mar 6, 2021
  168. New activation functions in mlsauce's LSBoost Feb 12, 2021
  169. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  170. A deeper learning architecture in nnetsauce Dec 18, 2020
  171. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  172. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  173. Generalized nonlinear models in nnetsauce Nov 28, 2020
  174. Boosting nonlinear penalized least squares Nov 21, 2020
  175. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  176. NEWS Oct 30, 2020
  177. A glimpse into my PhD journey Oct 23, 2020
  178. Submitting R package to CRAN Oct 16, 2020
  179. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  180. Forecasting lung disease progression Oct 2, 2020
  181. New nnetsauce Sep 25, 2020
  182. Technical documentation Sep 18, 2020
  183. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  184. Back next week, and a few announcements Sep 4, 2020
  185. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  186. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  187. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  188. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  189. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  190. Announcements Jun 26, 2020
  191. Parallel AdaOpt classification Jun 19, 2020
  192. Comments section and other news Jun 12, 2020
  193. Maximizing your tip as a waiter Jun 5, 2020
  194. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  195. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  196. AdaOpt May 15, 2020
  197. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  198. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  199. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  200. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  201. Grid search cross-validation using crossval Apr 10, 2020
  202. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  203. Time series cross-validation using crossval Mar 27, 2020
  204. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  205. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  206. R notebooks for nnetsauce Mar 6, 2020
  207. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  208. Create a specific feed in your Jekyll blog Feb 21, 2020
  209. Git/Github for contributing to package development Feb 14, 2020
  210. Feedback forms for contributing Feb 7, 2020
  211. nnetsauce for R Jan 31, 2020
  212. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  213. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  214. Search bar, new year 2020 Jan 10, 2020
  215. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  216. Understanding model interactions with the `teller` Dec 13, 2019
  217. Using the `teller` on a classifier Dec 6, 2019
  218. Benchmarking the querier's verbs Nov 29, 2019
  219. Composing the querier's verbs for data wrangling Nov 22, 2019
  220. Comparing and explaining model predictions with the teller Nov 15, 2019
  221. Tests for the significance of marginal effects in the teller Nov 8, 2019
  222. Introducing the teller Nov 1, 2019
  223. Introducing the querier Oct 25, 2019
  224. Prediction intervals for nnetsauce models Oct 18, 2019
  225. Using R in Python for statistical learning/data science Oct 11, 2019
  226. Model calibration with `crossval` Oct 4, 2019
  227. Bagging in the nnetsauce Sep 25, 2019
  228. Adaboost learning with nnetsauce Sep 18, 2019
  229. Change in blog's presentation Sep 4, 2019
  230. nnetsauce on Pypi Jun 5, 2019
  231. More nnetsauce (examples of use) May 9, 2019
  232. nnetsauce Mar 13, 2019
  233. crossval Mar 13, 2019
  234. 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. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  2. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  3. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  4. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  5. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  6. Backpropagating quasi-randomized neural networks
  7. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  8. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  9. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  10. Free R, Python and SQL editors in techtonique dot net
  11. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  12. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  13. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  14. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  15. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  16. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis
  17. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  18. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  19. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  20. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  21. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  22. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  23. Survival stacking: survival analysis translated as supervised classification in R and Python
  24. Extending the Theta forecasting method to GLMs and attention
  25. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  26. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  27. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  28. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  29. Web app with DeepSeek R1 and Hugging Face API for chatting
  30. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  31. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  32. Command Line Interface (CLI) for techtonique.net's API
  33. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  34. 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
  35. Techtonique is out! (with a tutorial in various programming languages and formats)
  36. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  37. 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?
  38. 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)
  39. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  40. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  41. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  42. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  43. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  44. GLMNet in Python: Generalized Linear Models
  45. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  46. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  47. Chat with your tabular data in www.techtonique.net
  48. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  49. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  50. R and Python consoles + JupyterLite in www.techtonique.net
  51. Gradient-Boosting anything (alert: high performance)
  52. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  53. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  54. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  55. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  56. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  57. New versions of nnetsauce and ahead
  58. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  59. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  60. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  61. Copulas for uncertainty quantification in time series forecasting
  62. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  63. learningmachine for Python (new version)
  64. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  65. 10 uncertainty quantification methods in nnetsauce forecasting
  66. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  67. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  68. Automated hyperparameter tuning using any conformalized surrogate
  69. Recognizing handwritten digits with Ridge2Classifier
  70. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  71. Probability of receiving a loan; using learningmachine
  72. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  73. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  74. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  75. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  76. learningmachine v1.1.2: for Python
  77. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  78. rtopy (v0.1.1): calling R functions in Python
  79. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  80. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  81. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  82. Tuning Machine Learning models with GPopt's new version Part 2
  83. Tuning Machine Learning models with GPopt's new version
  84. Subsampling continuous and discrete response variables
  85. DeepMTS, a Deep Learning Model for Multivariate Time Series
  86. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  87. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  88. Diffusion models in Python with esgtoolkit (Part2)
  89. Diffusion models in Python with esgtoolkit
  90. Quasi-randomized nnetworks in Julia, Python and R
  91. A plethora of datasets at your fingertips
  92. A classifier that's very accurate (and deep)
  93. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  94. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  95. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  96. Version v0.14.0 of nnetsauce for R and Python
  97. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  98. (News from) forecasting in Python with ahead (progress bars and plots)
  99. Forecasting in Python with ahead
  100. Did you ask ChatGPT about who you are?
  101. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  102. Simple interfaces to the forecasting API
  103. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  104. Prediction intervals (not only) for Boosted Configuration Networks in Python
  105. A Machine Learning workflow using Techtonique
  106. Super Mario Bros © in the browser using PyScript
  107. News from ESGtoolkit, ycinterextra, and nnetsauce
  108. Explaining a Keras _neural_ network predictions with the-teller
  109. New version of nnetsauce -- various quasi-randomized networks
  110. A dashboard illustrating bivariate time series forecasting with `ahead`
  111. Forecasting with `ahead` (Python version)
  112. Tuning and interpreting LSBoost
  113. Classification using linear regression
  114. Documentation and source code for GPopt, a package for Bayesian optimization
  115. Hyperparameters tuning with GPopt
  116. A forecasting tool (API) with examples in curl, R, Python
  117. Bayesian Optimization with GPopt Part 2 (save and resume)
  118. Bayesian Optimization with GPopt
  119. Compatibility of nnetsauce and mlsauce with scikit-learn
  120. Explaining xgboost predictions with the teller
  121. An infinity of time series models in nnetsauce
  122. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  123. A deeper learning architecture in nnetsauce
  124. Generalized nonlinear models in nnetsauce
  125. Boosting nonlinear penalized least squares
  126. Technical documentation
  127. A new version of nnetsauce, and a new Techtonique website
  128. Back next week, and a few announcements
  129. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  130. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  131. nnetsauce version 0.5.0, randomized neural networks on GPU
  132. Maximizing your tip as a waiter (Part 2)
  133. Parallel AdaOpt classification
  134. Maximizing your tip as a waiter
  135. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  136. AdaOpt
  137. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  138. Encoding your categorical variables based on the response variable and correlations
  139. Documentation for the querier, a query language for Data Frames
  140. Import data into the querier (now on Pypi), a query language for Data Frames
  141. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  142. A new version of nnetsauce (v0.3.1)
  143. Using R in Python for statistical learning/data science
  144. 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. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  2. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  3. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  4. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  5. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  6. R version of 'Backpropagating quasi-randomized neural networks'
  7. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  8. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  9. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  10. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  11. Free R, Python and SQL editors in techtonique dot net
  12. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  13. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  14. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  15. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  16. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis
  17. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  18. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  19. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  20. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  21. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  22. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  23. Survival stacking: survival analysis translated as supervised classification in R and Python
  24. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  25. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  26. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  27. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  28. Extending the Theta forecasting method to GLMs and attention
  29. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  30. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  31. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  32. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  33. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  34. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  35. CRAN-like repository for most recent releases of Techtonique's R packages
  36. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  37. Web app with DeepSeek R1 and Hugging Face API for chatting
  38. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  39. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  40. A simple test of the martingale hypothesis in esgtoolkit
  41. Command Line Interface (CLI) for techtonique.net's API
  42. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  43. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  44. Techtonique is out! (with a tutorial in various programming languages and formats)
  45. 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?
  46. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  47. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  48. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  49. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  50. GLMNet in Python: Generalized Linear Models
  51. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  52. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  53. Chat with your tabular data in www.techtonique.net
  54. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  55. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  56. R and Python consoles + JupyterLite in www.techtonique.net
  57. Gradient-Boosting anything (alert: high performance): Part2, R version
  58. Gradient-Boosting anything (alert: high performance)
  59. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  60. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  61. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  62. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  63. New versions of nnetsauce and ahead
  64. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  65. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  66. R package development workflow (assuming you're using macOS or Linux)
  67. A new method for deriving a nonparametric confidence interval for the mean
  68. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  69. Bayesian (nonlinear) adaptive learning
  70. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  71. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  72. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  73. Forecasting the Economy
  74. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  75. Probability of receiving a loan; using learningmachine
  76. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  77. Conformalized predictive simulations for univariate time series on more than 250 data sets
  78. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  79. Multiple examples of Machine Learning forecasting with ahead
  80. rtopy (v0.1.1): calling R functions in Python
  81. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  82. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  83. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  84. 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
  85. Diffusion models in Python with esgtoolkit (Part2)
  86. Diffusion models in Python with esgtoolkit
  87. Quasi-randomized nnetworks in Julia, Python and R
  88. A plethora of datasets at your fingertips
  89. A classifier that's very accurate (and deep)
  90. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  91. Version v0.14.0 of nnetsauce for R and Python
  92. A diffusion model: G2++
  93. Diffusion models in ESGtoolkit + announcements
  94. Risk-neutralize simulations
  95. Comparing cross-validation results using crossval_ml and boxplots
  96. Did you ask ChatGPT about who you are?
  97. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  98. Simple interfaces to the forecasting API
  99. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  100. Boosted Configuration (neural) Networks Pt. 2
  101. Boosted Configuration (_neural_) Networks for classification
  102. News from ESGtoolkit, ycinterextra, and nnetsauce
  103. New version of nnetsauce -- various quasi-randomized networks
  104. A dashboard illustrating bivariate time series forecasting with `ahead`
  105. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  106. Time series cross-validation using `crossvalidation` (Part 2)
  107. Fast and scalable forecasting with ahead::ridge2f
  108. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  109. Forecasting with `ahead`
  110. `crossvalidation` and random search for calibrating support vector machines
  111. parallel grid search cross-validation using `crossvalidation`
  112. `crossvalidation` on R-universe, plus a classification example
  113. A forecasting tool (API) with examples in curl, R, Python
  114. An infinity of time series models in nnetsauce
  115. New activation functions in mlsauce's LSBoost
  116. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  117. Classify penguins with nnetsauce's MultitaskClassifier
  118. Bayesian forecasting for uni/multivariate time series
  119. Boosting nonlinear penalized least squares
  120. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  121. Submitting R package to CRAN
  122. Simulation of dependent variables in ESGtoolkit
  123. Forecasting lung disease progression
  124. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  125. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  126. nnetsauce version 0.5.0, randomized neural networks on GPU
  127. Maximizing your tip as a waiter (Part 2)
  128. Maximizing your tip as a waiter
  129. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  130. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  131. Custom errors for cross-validation using crossval::crossval_ml
  132. Encoding your categorical variables based on the response variable and correlations
  133. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  134. Grid search cross-validation using crossval
  135. Time series cross-validation using crossval
  136. On model specification, identification, degrees of freedom and regularization
  137. R notebooks for nnetsauce
  138. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  139. Feedback forms for contributing
  140. nnetsauce for R
  141. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  142. Using R in Python for statistical learning/data science
  143. Model calibration with `crossval`
  144. crossval