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Blogroll | Top

List of posts by date of publication | Top

  1. genbooster, mlsauce, XGBoost, LightGBM — untuned small benchmark Nov 1, 2026
  2. caretS3 and ahead for Tabular and Time Series Machine Learning Oct 25, 2026
  3. Choosing the size of the calibration set in conformal prediction with ahead::conformalize() Oct 18, 2026
  4. Machine-learning loss reserving vs Mack Chain-Ladder under settlement speed-up and calendar inflation (Python version) Oct 5, 2026
  5. mlreserve: machine-learning loss reserving on 'real-world' triangles (based on a 'ChainLadder' fork) Oct 3, 2026
  6. Analytics Pipeline for Dashboards, with Python, R and Javascript Sep 27, 2026
  7. Python version of Semi-parametric option pricing based on underlying's historical data (accepted at the osQF 2026 (ex R/Finance) conference) Sep 21, 2026
  8. Semi-parametric option pricing based on underlying's historical data (accepted at the osQF 2026 (ex R/Finance) conference) Sep 20, 2026
  9. Model-agnostic prediction intervals in Python and R: does nnetsauce's QuantileRegressor hold up? Sep 14, 2026
  10. ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional Sep 8, 2026
  11. Skip the R/Python runtime: fast tabular dashboards with Observable Framework Aug 31, 2026
  12. PCARVFL vs CTGAN for synthetic tabular data generation on an insurance pricing dataset Aug 22, 2026
  13. 'Zero-Shot Probabilistic Stock Returns Forecasting with Pretrained RVFL Networks' accepted at COPA 2026 (and to appear in the Proceedings of Machine Learning Research) Aug 15, 2026
  14. 'PCARVFLSimulator': a GAN-like tabular data synthesizer built from PCA scores, a Random Vector Functional-Link network, and residuals bootstrapping Aug 10, 2026
  15. 'garchf': GARCH probabilistic forecasting with package 'forecast'-style interface (and 'rugarch' under the hood) Aug 1, 2026
  16. GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning Jul 26, 2026
  17. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R Jul 13, 2026
  18. Natively Interpretable Boosting Jul 12, 2026
  19. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus Jun 27, 2026
  20. Using scikit-learn models in R easily with the tisthemachinelearner package Jun 21, 2026
  21. No-Code Machine Learning in Excel with the Techtonique API Jun 14, 2026
  22. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3 Jun 7, 2026
  23. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now) May 31, 2026
  24. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R May 21, 2026
  25. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R May 17, 2026
  26. Probabilistic Time Series Cross-Validation with R package crossvalidation May 16, 2026
  27. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0 May 9, 2026
  28. You Don't Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis May 2, 2026
  29. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2 Apr 28, 2026
  30. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages Apr 26, 2026
  31. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets Apr 19, 2026
  32. `mlS3` — A Unified S3 Machine Learning Interface in R Apr 12, 2026
  33. One interface, (Almost) Every Classifier: unifiedml v0.2.1 Apr 4, 2026
  34. Techtonique dot net is down until further notice Apr 1, 2026
  35. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors) Mar 29, 2026
  36. Python version of 'Option pricing using time series models as market price of risk Pt.3' Mar 22, 2026
  37. Option pricing using time series models as market price of risk Pt.3 Mar 16, 2026
  38. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios) Mar 8, 2026
  39. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting Mar 1, 2026
  40. nnetsauce with and without jax for GPU acceleration Feb 23, 2026
  41. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters Feb 16, 2026
  42. R version of Python package survivalist, for model-agnostic survival analysis Feb 9, 2026
  43. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026) Feb 4, 2026
  44. Option pricing using time series models as market price of risk Feb 1, 2026
  45. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f) Jan 31, 2026
  46. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor Jan 29, 2026
  47. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling Jan 25, 2026
  48. GPopt for Machine Learning (hyperparameters' tuning) Jan 21, 2026
  49. rtopy: an R to Python bridge -- novelties Jan 8, 2026
  50. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Jan 3, 2026
  51. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition Jan 1, 2026
  52. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce Dec 17, 2025
  53. Overfitting Random Fourier Features: Universal Approximation Property Dec 13, 2025
  54. Counterfactual Scenario Analysis with ahead::ridge2f Dec 11, 2025
  55. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce Dec 10, 2025
  56. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint) Dec 7, 2025
  57. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis) Dec 3, 2025
  58. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn Dec 1, 2025
  59. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv Nov 21, 2025
  60. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans Nov 16, 2025
  61. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning Nov 13, 2025
  62. unifiedml in R: A Unified Machine Learning Interface Nov 5, 2025
  63. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates) Oct 28, 2025
  64. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation Oct 27, 2025
  65. RPweave: Unified R + Python + LaTeX System using uv Oct 19, 2025
  66. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator Oct 19, 2025
  67. R port of llama2.c Oct 9, 2025
  68. Native uncertainty quantification for time series with NGBoost Oct 8, 2025
  69. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving Oct 6, 2025
  70. Real-time pricing with a pretrained probabilistic stock return model Oct 1, 2025
  71. Combining any model with GARCH(1,1) for probabilistic stock forecasting Sep 23, 2025
  72. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R Sep 21, 2025
  73. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning Sep 16, 2025
  74. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation Sep 9, 2025
  75. Transfer Learning using ahead::ridge2f on synthetic stocks returns Sep 8, 2025
  76. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London... Sep 4, 2025
  77. external regressors in ahead::dynrmf's interface for Machine learning forecasting Sep 1, 2025
  78. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Aug 20, 2025
  79. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting Jul 26, 2025
  80. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization) Jul 15, 2025
  81. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI. Jul 10, 2025
  82. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics). Jul 8, 2025
  83. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting Jul 7, 2025
  84. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting Jul 6, 2025
  85. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting Jul 5, 2025
  86. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting Jul 4, 2025
  87. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting Jul 3, 2025
  88. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting Jul 2, 2025
  89. 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
  90. R version of 'Backpropagating quasi-randomized neural networks' Jun 24, 2025
  91. Backpropagating quasi-randomized neural networks Jun 23, 2025
  92. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting Jun 21, 2025
  93. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf Jun 18, 2025
  94. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters Jun 14, 2025
  95. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models Jun 13, 2025
  96. Free R, Python and SQL editors in techtonique dot net Jun 9, 2025
  97. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation Jun 7, 2025
  98. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 6, 2025
  99. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 5, 2025
  100. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 4, 2025
  101. 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
  102. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 2, 2025
  103. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line) Jun 1, 2025
  104. 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
  105. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios May 30, 2025
  106. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose May 29, 2025
  107. 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
  108. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
  109. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
  110. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
  111. Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
  112. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
  113. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
  114. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
  115. 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
  116. Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
  117. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
  118. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
  119. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
  120. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
  121. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
  122. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
  123. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
  124. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
  125. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
  126. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
  127. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  128. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  129. 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
  130. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  131. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  132. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  133. 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
  134. Techtonique is released! (with a tutorial in various programming languages and formats) Jan 14, 2025
  135. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  136. 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
  137. 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
  138. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  139. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  140. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  141. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  142. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  143. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  144. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  145. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  146. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  147. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  148. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  149. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  150. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  151. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  152. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  153. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  154. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  155. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  156. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  157. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  158. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  159. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  160. New versions of nnetsauce and ahead Sep 9, 2024
  161. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  162. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  163. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  164. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  165. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  166. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  167. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  168. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  169. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  170. learningmachine for Python (new version) Jul 15, 2024
  171. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  172. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  173. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  174. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  175. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  176. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  177. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  178. Forecasting the Economy May 27, 2024
  179. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  180. Probability of receiving a loan; using learningmachine May 12, 2024
  181. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  182. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  183. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  184. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  185. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  186. learningmachine v1.1.2: for Python Apr 1, 2024
  187. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  188. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  189. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  190. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  191. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  192. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  193. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  194. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  195. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  196. Subsampling continuous and discrete response variables Jan 22, 2024
  197. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  198. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  199. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  200. 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
  201. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  202. Diffusion models in Python with esgtoolkit Dec 11, 2023
  203. Julia packaging at the command line Dec 4, 2023
  204. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  205. A plethora of datasets at your fingertips Nov 20, 2023
  206. A classifier that's very accurate (and deep) Nov 12, 2023
  207. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  208. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  209. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  210. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  211. A diffusion model: G2++ Oct 9, 2023
  212. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  213. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  214. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  215. Forecasting in Python with ahead Sep 11, 2023
  216. Risk-neutralize simulations Sep 4, 2023
  217. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  218. Reminder Apr 30, 2023
  219. Did you ask ChatGPT about who you are? Apr 16, 2023
  220. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  221. Simple interfaces to the forecasting API Nov 23, 2022
  222. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  223. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  224. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  225. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  226. A Machine Learning workflow using Techtonique Jun 6, 2022
  227. Super Mario Bros © in the browser using PyScript May 8, 2022
  228. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  229. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  230. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  231. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  232. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  233. Forecasting with `ahead` (Python version) Dec 13, 2021
  234. Tuning and interpreting LSBoost Nov 15, 2021
  235. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  236. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  237. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  238. Forecasting with `ahead` Oct 15, 2021
  239. Classification using linear regression Sep 26, 2021
  240. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  241. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  242. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  243. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  244. Hyperparameters tuning with GPopt Jun 11, 2021
  245. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  246. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  247. Bayesian Optimization with GPopt Apr 16, 2021
  248. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  249. Explaining xgboost predictions with the teller Mar 12, 2021
  250. An infinity of time series models in nnetsauce Mar 6, 2021
  251. New activation functions in mlsauce's LSBoost Feb 12, 2021
  252. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  253. A deeper learning architecture in nnetsauce Dec 18, 2020
  254. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  255. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  256. Generalized nonlinear models in nnetsauce Nov 28, 2020
  257. Boosting nonlinear penalized least squares Nov 21, 2020
  258. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  259. NEWS Oct 30, 2020
  260. A glimpse into my PhD journey Oct 23, 2020
  261. Submitting R package to CRAN Oct 16, 2020
  262. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  263. Forecasting lung disease progression Oct 2, 2020
  264. New nnetsauce Sep 25, 2020
  265. Technical documentation Sep 18, 2020
  266. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  267. Back next week, and a few announcements Sep 4, 2020
  268. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  269. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  270. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  271. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  272. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  273. Announcements Jun 26, 2020
  274. Parallel AdaOpt classification Jun 19, 2020
  275. Comments section and other news Jun 12, 2020
  276. Maximizing your tip as a waiter Jun 5, 2020
  277. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  278. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  279. AdaOpt May 15, 2020
  280. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  281. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  282. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  283. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  284. Grid search cross-validation using crossval Apr 10, 2020
  285. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  286. Time series cross-validation using crossval Mar 27, 2020
  287. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  288. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  289. R notebooks for nnetsauce Mar 6, 2020
  290. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  291. Create a specific feed in your Jekyll blog Feb 21, 2020
  292. Git/Github for contributing to package development Feb 14, 2020
  293. Feedback forms for contributing Feb 7, 2020
  294. nnetsauce for R Jan 31, 2020
  295. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  296. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  297. Search bar, new year 2020 Jan 10, 2020
  298. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  299. Understanding model interactions with the `teller` Dec 13, 2019
  300. Using the `teller` on a classifier Dec 6, 2019
  301. Benchmarking the querier's verbs Nov 29, 2019
  302. Composing the querier's verbs for data wrangling Nov 22, 2019
  303. Comparing and explaining model predictions with the teller Nov 15, 2019
  304. Tests for the significance of marginal effects in the teller Nov 8, 2019
  305. Introducing the teller Nov 1, 2019
  306. Introducing the querier Oct 25, 2019
  307. Prediction intervals for nnetsauce models Oct 18, 2019
  308. Using R in Python for statistical learning/data science Oct 11, 2019
  309. Model calibration with `crossval` Oct 4, 2019
  310. Bagging in the nnetsauce Sep 25, 2019
  311. Adaboost learning with nnetsauce Sep 18, 2019
  312. Change in blog's presentation Sep 4, 2019
  313. nnetsauce on Pypi Jun 5, 2019
  314. More nnetsauce (examples of use) May 9, 2019
  315. nnetsauce Mar 13, 2019
  316. crossval Mar 13, 2019
  317. test Mar 10, 2019

Categories | Top

Forecasting

  1. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  2. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  3. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  4. 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
  5. Techtonique is released! (with a tutorial in various programming languages and formats)
  6. 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?
  7. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  8. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  9. 10 uncertainty quantification methods in nnetsauce forecasting
  10. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  11. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  12. DeepMTS, a Deep Learning Model for Multivariate Time Series
  13. Version v0.14.0 of nnetsauce for R and Python
  14. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  15. (News from) forecasting in Python with ahead (progress bars and plots)
  16. Forecasting in Python with ahead
  17. Simple interfaces to the forecasting API
  18. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  19. A dashboard illustrating bivariate time series forecasting with `ahead`
  20. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret

Misc

  1. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  2. 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
  3. Julia packaging at the command line
  4. A plethora of datasets at your fingertips
  5. Risk-neutralize simulations
  6. Comparing cross-validation results using crossval_ml and boxplots
  7. Reminder
  8. Did you ask ChatGPT about who you are?
  9. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  10. Boosted Configuration (_neural_) Networks for classification
  11. Super Mario Bros © in the browser using PyScript
  12. News from ESGtoolkit, ycinterextra, and nnetsauce
  13. Time series cross-validation using `crossvalidation` (Part 2)
  14. Fast and scalable forecasting with ahead::ridge2f
  15. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  16. Forecasting with `ahead`
  17. Documentation and source code for GPopt, a package for Bayesian optimization
  18. Hyperparameters tuning with GPopt
  19. A forecasting tool (API) with examples in curl, R, Python
  20. Bayesian Optimization with GPopt Part 2 (save and resume)
  21. Bayesian Optimization with GPopt
  22. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  23. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  24. NEWS
  25. A glimpse into my PhD journey
  26. Forecasting lung disease progression
  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. Maximizing your tip as a waiter (Part 2)
  32. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  33. Announcements
  34. Comments section and other news
  35. Maximizing your tip as a waiter
  36. Custom errors for cross-validation using crossval::crossval_ml
  37. Encoding your categorical variables based on the response variable and correlations
  38. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  39. Grid search cross-validation using crossval
  40. Time series cross-validation using crossval
  41. On model specification, identification, degrees of freedom and regularization
  42. Create a specific feed in your Jekyll blog
  43. Git/Github for contributing to package development
  44. Feedback forms for contributing
  45. Change in blog's presentation
  46. test

Python

  1. genbooster, mlsauce, XGBoost, LightGBM — untuned small benchmark
  2. Machine-learning loss reserving vs Mack Chain-Ladder under settlement speed-up and calendar inflation (Python version)
  3. Analytics Pipeline for Dashboards, with Python, R and Javascript
  4. Python version of Semi-parametric option pricing based on underlying's historical data (accepted at the osQF 2026 (ex R/Finance) conference)
  5. Model-agnostic prediction intervals in Python and R: does nnetsauce's QuantileRegressor hold up?
  6. ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional
  7. Skip the R/Python runtime: fast tabular dashboards with Observable Framework
  8. PCARVFL vs CTGAN for synthetic tabular data generation on an insurance pricing dataset
  9. 'PCARVFLSimulator': a GAN-like tabular data synthesizer built from PCA scores, a Random Vector Functional-Link network, and residuals bootstrapping
  10. Natively Interpretable Boosting
  11. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  12. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  13. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  14. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  15. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2
  16. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages
  17. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets
  18. Techtonique dot net is down until further notice
  19. Python version of 'Option pricing using time series models as market price of risk Pt.3'
  20. nnetsauce with and without jax for GPU acceleration
  21. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  22. R version of Python package survivalist, for model-agnostic survival analysis
  23. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor
  24. GPopt for Machine Learning (hyperparameters' tuning)
  25. rtopy: an R to Python bridge -- novelties
  26. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  27. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  28. Overfitting Random Fourier Features: Universal Approximation Property
  29. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce
  30. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  31. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  32. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  33. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  34. RPweave: Unified R + Python + LaTeX System using uv
  35. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  36. Native uncertainty quantification for time series with NGBoost
  37. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  38. Real-time pricing with a pretrained probabilistic stock return model
  39. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  40. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  41. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  42. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  43. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  44. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  45. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  46. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  47. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  48. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  49. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  50. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  51. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  52. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  53. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  54. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  55. Backpropagating quasi-randomized neural networks
  56. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  57. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  58. Free R, Python and SQL editors in techtonique dot net
  59. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  60. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  61. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  62. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  63. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  64. 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
  65. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  66. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  67. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  68. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  69. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  70. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  71. Survival stacking: survival analysis translated as supervised classification in R and Python
  72. Extending the Theta forecasting method to GLMs and attention
  73. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  74. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  75. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  76. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  77. Web app with DeepSeek R1 and Hugging Face API for chatting
  78. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  79. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  80. Command Line Interface (CLI) for techtonique.net's API
  81. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  82. 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
  83. Techtonique is released! (with a tutorial in various programming languages and formats)
  84. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  85. 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?
  86. 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)
  87. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  88. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  89. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  90. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  91. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  92. GLMNet in Python: Generalized Linear Models
  93. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  94. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  95. Chat with your tabular data in www.techtonique.net
  96. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  97. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  98. R and Python consoles + JupyterLite in www.techtonique.net
  99. Gradient-Boosting anything (alert: high performance)
  100. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  101. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  102. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  103. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  104. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  105. New versions of nnetsauce and ahead
  106. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  107. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  108. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  109. Copulas for uncertainty quantification in time series forecasting
  110. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  111. learningmachine for Python (new version)
  112. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  113. 10 uncertainty quantification methods in nnetsauce forecasting
  114. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  115. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  116. Automated hyperparameter tuning using any conformalized surrogate
  117. Recognizing handwritten digits with Ridge2Classifier
  118. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  119. Probability of receiving a loan; using learningmachine
  120. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  121. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  122. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  123. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  124. learningmachine v1.1.2: for Python
  125. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  126. rtopy (v0.1.1): calling R functions in Python
  127. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  128. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  129. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  130. Tuning Machine Learning models with GPopt's new version Part 2
  131. Tuning Machine Learning models with GPopt's new version
  132. Subsampling continuous and discrete response variables
  133. DeepMTS, a Deep Learning Model for Multivariate Time Series
  134. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  135. 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
  136. Diffusion models in Python with esgtoolkit (Part2)
  137. Diffusion models in Python with esgtoolkit
  138. Quasi-randomized nnetworks in Julia, Python and R
  139. A plethora of datasets at your fingertips
  140. A classifier that's very accurate (and deep)
  141. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  142. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  143. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  144. Version v0.14.0 of nnetsauce for R and Python
  145. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  146. (News from) forecasting in Python with ahead (progress bars and plots)
  147. Forecasting in Python with ahead
  148. Did you ask ChatGPT about who you are?
  149. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  150. Simple interfaces to the forecasting API
  151. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  152. Prediction intervals (not only) for Boosted Configuration Networks in Python
  153. A Machine Learning workflow using Techtonique
  154. Super Mario Bros © in the browser using PyScript
  155. News from ESGtoolkit, ycinterextra, and nnetsauce
  156. Explaining a Keras _neural_ network predictions with the-teller
  157. New version of nnetsauce -- various quasi-randomized networks
  158. A dashboard illustrating bivariate time series forecasting with `ahead`
  159. Forecasting with `ahead` (Python version)
  160. Tuning and interpreting LSBoost
  161. Classification using linear regression
  162. Documentation and source code for GPopt, a package for Bayesian optimization
  163. Hyperparameters tuning with GPopt
  164. A forecasting tool (API) with examples in curl, R, Python
  165. Bayesian Optimization with GPopt Part 2 (save and resume)
  166. Bayesian Optimization with GPopt
  167. Compatibility of nnetsauce and mlsauce with scikit-learn
  168. Explaining xgboost predictions with the teller
  169. An infinity of time series models in nnetsauce
  170. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  171. A deeper learning architecture in nnetsauce
  172. Generalized nonlinear models in nnetsauce
  173. Boosting nonlinear penalized least squares
  174. Technical documentation
  175. A new version of nnetsauce, and a new Techtonique website
  176. Back next week, and a few announcements
  177. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  178. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  179. nnetsauce version 0.5.0, randomized neural networks on GPU
  180. Maximizing your tip as a waiter (Part 2)
  181. Parallel AdaOpt classification
  182. Maximizing your tip as a waiter
  183. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  184. AdaOpt
  185. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  186. Encoding your categorical variables based on the response variable and correlations
  187. Documentation for the querier, a query language for Data Frames
  188. Import data into the querier (now on Pypi), a query language for Data Frames
  189. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  190. A new version of nnetsauce (v0.3.1)
  191. Using R in Python for statistical learning/data science
  192. 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. caretS3 and ahead for Tabular and Time Series Machine Learning
  2. Choosing the size of the calibration set in conformal prediction with ahead::conformalize()
  3. Machine-learning loss reserving vs Mack Chain-Ladder under settlement speed-up and calendar inflation (Python version)
  4. mlreserve: machine-learning loss reserving on 'real-world' triangles (based on a 'ChainLadder' fork)
  5. Analytics Pipeline for Dashboards, with Python, R and Javascript
  6. Semi-parametric option pricing based on underlying's historical data (accepted at the osQF 2026 (ex R/Finance) conference)
  7. Model-agnostic prediction intervals in Python and R: does nnetsauce's QuantileRegressor hold up?
  8. ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional
  9. Skip the R/Python runtime: fast tabular dashboards with Observable Framework
  10. 'Zero-Shot Probabilistic Stock Returns Forecasting with Pretrained RVFL Networks' accepted at COPA 2026 (and to appear in the Proceedings of Machine Learning Research)
  11. 'garchf': GARCH probabilistic forecasting with package 'forecast'-style interface (and 'rugarch' under the hood)
  12. GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning
  13. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R
  14. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  15. Using scikit-learn models in R easily with the tisthemachinelearner package
  16. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3
  17. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  18. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  19. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  20. Probabilistic Time Series Cross-Validation with R package crossvalidation
  21. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0
  22. You Don't Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis
  23. `mlS3` — A Unified S3 Machine Learning Interface in R
  24. One interface, (Almost) Every Classifier: unifiedml v0.2.1
  25. Techtonique dot net is down until further notice
  26. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors)
  27. Option pricing using time series models as market price of risk Pt.3
  28. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios)
  29. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting
  30. nnetsauce with and without jax for GPU acceleration
  31. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  32. R version of Python package survivalist, for model-agnostic survival analysis
  33. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026)
  34. Option pricing using time series models as market price of risk
  35. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f)
  36. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling
  37. rtopy: an R to Python bridge -- novelties
  38. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  39. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition
  40. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  41. Overfitting Random Fourier Features: Universal Approximation Property
  42. Counterfactual Scenario Analysis with ahead::ridge2f
  43. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  44. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  45. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  46. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  47. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans
  48. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning
  49. unifiedml in R: A Unified Machine Learning Interface
  50. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates)
  51. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  52. RPweave: Unified R + Python + LaTeX System using uv
  53. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  54. R port of llama2.c
  55. Native uncertainty quantification for time series with NGBoost
  56. Real-time pricing with a pretrained probabilistic stock return model
  57. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  58. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  59. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  60. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  61. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  62. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  63. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  64. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  65. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  66. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  67. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  68. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  69. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  70. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  71. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  72. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  73. R version of 'Backpropagating quasi-randomized neural networks'
  74. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  75. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  76. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  77. Free R, Python and SQL editors in techtonique dot net
  78. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  79. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  80. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  81. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  82. 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
  83. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  84. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  85. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  86. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  87. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  88. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  89. Survival stacking: survival analysis translated as supervised classification in R and Python
  90. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  91. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  92. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  93. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  94. Extending the Theta forecasting method to GLMs and attention
  95. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  96. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  97. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  98. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  99. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  100. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  101. CRAN-like repository for most recent releases of Techtonique's R packages
  102. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  103. Web app with DeepSeek R1 and Hugging Face API for chatting
  104. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  105. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  106. A simple test of the martingale hypothesis in esgtoolkit
  107. Command Line Interface (CLI) for techtonique.net's API
  108. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  109. 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
  110. Techtonique is released! (with a tutorial in various programming languages and formats)
  111. 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?
  112. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  113. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  114. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  115. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  116. GLMNet in Python: Generalized Linear Models
  117. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  118. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  119. Chat with your tabular data in www.techtonique.net
  120. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  121. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  122. R and Python consoles + JupyterLite in www.techtonique.net
  123. Gradient-Boosting anything (alert: high performance): Part2, R version
  124. Gradient-Boosting anything (alert: high performance)
  125. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  126. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  127. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  128. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  129. New versions of nnetsauce and ahead
  130. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  131. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  132. R package development workflow (assuming you're using macOS or Linux)
  133. A new method for deriving a nonparametric confidence interval for the mean
  134. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  135. Bayesian (nonlinear) adaptive learning
  136. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  137. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  138. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  139. Forecasting the Economy
  140. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  141. Probability of receiving a loan; using learningmachine
  142. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  143. Conformalized predictive simulations for univariate time series on more than 250 data sets
  144. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  145. Multiple examples of Machine Learning forecasting with ahead
  146. rtopy (v0.1.1): calling R functions in Python
  147. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  148. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  149. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  150. 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
  151. Diffusion models in Python with esgtoolkit (Part2)
  152. Diffusion models in Python with esgtoolkit
  153. Quasi-randomized nnetworks in Julia, Python and R
  154. A plethora of datasets at your fingertips
  155. A classifier that's very accurate (and deep)
  156. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  157. Version v0.14.0 of nnetsauce for R and Python
  158. A diffusion model: G2++
  159. Diffusion models in ESGtoolkit + announcements
  160. Risk-neutralize simulations
  161. Comparing cross-validation results using crossval_ml and boxplots
  162. Did you ask ChatGPT about who you are?
  163. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  164. Simple interfaces to the forecasting API
  165. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  166. Boosted Configuration (neural) Networks Pt. 2
  167. Boosted Configuration (_neural_) Networks for classification
  168. News from ESGtoolkit, ycinterextra, and nnetsauce
  169. New version of nnetsauce -- various quasi-randomized networks
  170. A dashboard illustrating bivariate time series forecasting with `ahead`
  171. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  172. Time series cross-validation using `crossvalidation` (Part 2)
  173. Fast and scalable forecasting with ahead::ridge2f
  174. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  175. Forecasting with `ahead`
  176. `crossvalidation` and random search for calibrating support vector machines
  177. parallel grid search cross-validation using `crossvalidation`
  178. `crossvalidation` on R-universe, plus a classification example
  179. A forecasting tool (API) with examples in curl, R, Python
  180. An infinity of time series models in nnetsauce
  181. New activation functions in mlsauce's LSBoost
  182. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  183. Classify penguins with nnetsauce's MultitaskClassifier
  184. Bayesian forecasting for uni/multivariate time series
  185. Boosting nonlinear penalized least squares
  186. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  187. Submitting R package to CRAN
  188. Simulation of dependent variables in ESGtoolkit
  189. Forecasting lung disease progression
  190. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  191. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  192. nnetsauce version 0.5.0, randomized neural networks on GPU
  193. Maximizing your tip as a waiter (Part 2)
  194. Maximizing your tip as a waiter
  195. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  196. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  197. Custom errors for cross-validation using crossval::crossval_ml
  198. Encoding your categorical variables based on the response variable and correlations
  199. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  200. Grid search cross-validation using crossval
  201. Time series cross-validation using crossval
  202. On model specification, identification, degrees of freedom and regularization
  203. R notebooks for nnetsauce
  204. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  205. Feedback forms for contributing
  206. nnetsauce for R
  207. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  208. Using R in Python for statistical learning/data science
  209. Model calibration with `crossval`
  210. crossval