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Blogroll | Top

List of posts by date of publication | Top

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