logo

Blog


You can expect to read a post here every Sunday or Monday, mostly about Techtonique. Want to get notified? Subscribe via RSS.

A list of previous posts can be found below, and a list of categories here. Use the search bar appearing before this paragraph if you're looking for a more specific keyword.

You can also me on GitHub or Hire me on LinkedIn or Hire me on Malt or Hire me on Fiverr or Hire me on Upwork

Having any other inquiry related to the content published here? Easiest/fastest/safest way is to send an email to: thierry dot moudiki at gmail dot com.




Blogroll | Top


List of posts by date of publication | Top

  1. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  2. Real-time pricing with a pretrained probabilistic stock return model
  3. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  4. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  5. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  6. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  7. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  8. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  9. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  10. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  11. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  12. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  13. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  14. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  15. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  16. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  17. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  18. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  19. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  20. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  21. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  22. R version of 'Backpropagating quasi-randomized neural networks'
  23. Backpropagating quasi-randomized neural networks
  24. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  25. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  26. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  27. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  28. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  29. Free R, Python and SQL editors in techtonique dot net
  30. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  31. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  32. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  33. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  34. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  35. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  36. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  37. 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
  38. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  39. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  40. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  41. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  42. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  43. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  44. Survival stacking: survival analysis translated as supervised classification in R and Python
  45. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  46. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  47. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  48. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  49. Extending the Theta forecasting method to GLMs and attention
  50. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  51. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  52. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  53. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  54. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  55. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  56. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  57. CRAN-like repository for most recent releases of Techtonique's R packages
  58. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  59. Web app with DeepSeek R1 and Hugging Face API for chatting
  60. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  61. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  62. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  63. A simple test of the martingale hypothesis in esgtoolkit
  64. Command Line Interface (CLI) for techtonique.net's API
  65. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  66. 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
  67. Techtonique is out! (with a tutorial in various programming languages and formats)
  68. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  69. 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?
  70. 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)
  71. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  72. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  73. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  74. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  75. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  76. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  77. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  78. GLMNet in Python: Generalized Linear Models
  79. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  80. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  81. Chat with your tabular data in www.techtonique.net
  82. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  83. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  84. R and Python consoles + JupyterLite in www.techtonique.net
  85. Gradient-Boosting anything (alert: high performance): Part2, R version
  86. Gradient-Boosting anything (alert: high performance)
  87. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  88. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  89. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  90. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  91. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  92. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  93. New versions of nnetsauce and ahead
  94. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  95. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  96. R package development workflow (assuming you're using macOS or Linux)
  97. A new method for deriving a nonparametric confidence interval for the mean
  98. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  99. Bayesian (nonlinear) adaptive learning
  100. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  101. Copulas for uncertainty quantification in time series forecasting
  102. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  103. learningmachine for Python (new version)
  104. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  105. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  106. 10 uncertainty quantification methods in nnetsauce forecasting
  107. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  108. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  109. Automated hyperparameter tuning using any conformalized surrogate
  110. Recognizing handwritten digits with Ridge2Classifier
  111. Forecasting the Economy
  112. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  113. Probability of receiving a loan; using learningmachine
  114. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  115. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  116. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  117. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  118. Conformalized predictive simulations for univariate time series on more than 250 data sets
  119. learningmachine v1.1.2: for Python
  120. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  121. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  122. Multiple examples of Machine Learning forecasting with ahead
  123. rtopy (v0.1.1): calling R functions in Python
  124. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  125. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  126. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  127. Tuning Machine Learning models with GPopt's new version Part 2
  128. Tuning Machine Learning models with GPopt's new version
  129. Subsampling continuous and discrete response variables
  130. DeepMTS, a Deep Learning Model for Multivariate Time Series
  131. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  132. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  133. 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
  134. Diffusion models in Python with esgtoolkit (Part2)
  135. Diffusion models in Python with esgtoolkit
  136. Julia packaging at the command line
  137. Quasi-randomized nnetworks in Julia, Python and R
  138. A plethora of datasets at your fingertips
  139. A classifier that's very accurate (and deep)
  140. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  141. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  142. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  143. Version v0.14.0 of nnetsauce for R and Python
  144. A diffusion model: G2++
  145. Diffusion models in ESGtoolkit + announcements
  146. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  147. (News from) forecasting in Python with ahead (progress bars and plots)
  148. Forecasting in Python with ahead
  149. Risk-neutralize simulations
  150. Comparing cross-validation results using crossval_ml and boxplots
  151. Reminder
  152. Did you ask ChatGPT about who you are?
  153. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  154. Simple interfaces to the forecasting API
  155. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  156. Prediction intervals (not only) for Boosted Configuration Networks in Python
  157. Boosted Configuration (neural) Networks Pt. 2
  158. Boosted Configuration (_neural_) Networks for classification
  159. A Machine Learning workflow using Techtonique
  160. Super Mario Bros © in the browser using PyScript
  161. News from ESGtoolkit, ycinterextra, and nnetsauce
  162. Explaining a Keras _neural_ network predictions with the-teller
  163. New version of nnetsauce -- various quasi-randomized networks
  164. A dashboard illustrating bivariate time series forecasting with `ahead`
  165. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  166. Forecasting with `ahead` (Python version)
  167. Tuning and interpreting LSBoost
  168. Time series cross-validation using `crossvalidation` (Part 2)
  169. Fast and scalable forecasting with ahead::ridge2f
  170. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  171. Forecasting with `ahead`
  172. Classification using linear regression
  173. `crossvalidation` and random search for calibrating support vector machines
  174. parallel grid search cross-validation using `crossvalidation`
  175. `crossvalidation` on R-universe, plus a classification example
  176. Documentation and source code for GPopt, a package for Bayesian optimization
  177. Hyperparameters tuning with GPopt
  178. A forecasting tool (API) with examples in curl, R, Python
  179. Bayesian Optimization with GPopt Part 2 (save and resume)
  180. Bayesian Optimization with GPopt
  181. Compatibility of nnetsauce and mlsauce with scikit-learn
  182. Explaining xgboost predictions with the teller
  183. An infinity of time series models in nnetsauce
  184. New activation functions in mlsauce's LSBoost
  185. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  186. A deeper learning architecture in nnetsauce
  187. Classify penguins with nnetsauce's MultitaskClassifier
  188. Bayesian forecasting for uni/multivariate time series
  189. Generalized nonlinear models in nnetsauce
  190. Boosting nonlinear penalized least squares
  191. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  192. NEWS
  193. A glimpse into my PhD journey
  194. Submitting R package to CRAN
  195. Simulation of dependent variables in ESGtoolkit
  196. Forecasting lung disease progression
  197. New nnetsauce
  198. Technical documentation
  199. A new version of nnetsauce, and a new Techtonique website
  200. Back next week, and a few announcements
  201. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  202. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  203. nnetsauce version 0.5.0, randomized neural networks on GPU
  204. Maximizing your tip as a waiter (Part 2)
  205. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  206. Announcements
  207. Parallel AdaOpt classification
  208. Comments section and other news
  209. Maximizing your tip as a waiter
  210. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  211. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  212. AdaOpt
  213. Custom errors for cross-validation using crossval::crossval_ml
  214. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  215. Encoding your categorical variables based on the response variable and correlations
  216. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  217. Grid search cross-validation using crossval
  218. Documentation for the querier, a query language for Data Frames
  219. Time series cross-validation using crossval
  220. On model specification, identification, degrees of freedom and regularization
  221. Import data into the querier (now on Pypi), a query language for Data Frames
  222. R notebooks for nnetsauce
  223. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  224. Create a specific feed in your Jekyll blog
  225. Git/Github for contributing to package development
  226. Feedback forms for contributing
  227. nnetsauce for R
  228. A new version of nnetsauce (v0.3.1)
  229. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  230. Search bar, new year 2020
  231. 2019 Recap, the nnetsauce, the teller and the querier
  232. Understanding model interactions with the `teller`
  233. Using the `teller` on a classifier
  234. Benchmarking the querier's verbs
  235. Composing the querier's verbs for data wrangling
  236. Comparing and explaining model predictions with the teller
  237. Tests for the significance of marginal effects in the teller
  238. Introducing the teller
  239. Introducing the querier
  240. Prediction intervals for nnetsauce models
  241. Using R in Python for statistical learning/data science
  242. Model calibration with `crossval`
  243. Bagging in the nnetsauce
  244. Adaboost learning with nnetsauce
  245. Change in blog's presentation
  246. nnetsauce on Pypi
  247. More nnetsauce (examples of use)
  248. nnetsauce
  249. crossval
  250. test


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. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  2. Real-time pricing with a pretrained probabilistic stock return model
  3. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  4. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  5. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  6. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  7. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  8. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  9. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  10. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  11. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  12. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  13. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  14. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  15. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  16. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  17. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  18. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  19. Backpropagating quasi-randomized neural networks
  20. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  21. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  22. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  23. Free R, Python and SQL editors in techtonique dot net
  24. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  25. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  26. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  27. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  28. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  29. 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
  30. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  31. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  32. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  33. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  34. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  35. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  36. Survival stacking: survival analysis translated as supervised classification in R and Python
  37. Extending the Theta forecasting method to GLMs and attention
  38. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  39. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  40. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  41. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  42. Web app with DeepSeek R1 and Hugging Face API for chatting
  43. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  44. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  45. Command Line Interface (CLI) for techtonique.net's API
  46. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  47. 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
  48. Techtonique is out! (with a tutorial in various programming languages and formats)
  49. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  50. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  51. 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)
  52. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  53. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  54. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  55. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  56. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  57. GLMNet in Python: Generalized Linear Models
  58. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  59. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  60. Chat with your tabular data in www.techtonique.net
  61. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  62. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  63. R and Python consoles + JupyterLite in www.techtonique.net
  64. Gradient-Boosting anything (alert: high performance)
  65. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  66. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  67. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  68. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  69. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  70. New versions of nnetsauce and ahead
  71. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  72. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  73. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  74. Copulas for uncertainty quantification in time series forecasting
  75. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  76. learningmachine for Python (new version)
  77. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  78. 10 uncertainty quantification methods in nnetsauce forecasting
  79. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  80. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  81. Automated hyperparameter tuning using any conformalized surrogate
  82. Recognizing handwritten digits with Ridge2Classifier
  83. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  84. Probability of receiving a loan; using learningmachine
  85. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  86. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  87. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  88. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  89. learningmachine v1.1.2: for Python
  90. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  91. rtopy (v0.1.1): calling R functions in Python
  92. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  93. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  94. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  95. Tuning Machine Learning models with GPopt's new version Part 2
  96. Tuning Machine Learning models with GPopt's new version
  97. Subsampling continuous and discrete response variables
  98. DeepMTS, a Deep Learning Model for Multivariate Time Series
  99. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  100. 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
  101. Diffusion models in Python with esgtoolkit (Part2)
  102. Diffusion models in Python with esgtoolkit
  103. Quasi-randomized nnetworks in Julia, Python and R
  104. A plethora of datasets at your fingertips
  105. A classifier that's very accurate (and deep)
  106. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  107. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  108. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  109. Version v0.14.0 of nnetsauce for R and Python
  110. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  111. (News from) forecasting in Python with ahead (progress bars and plots)
  112. Forecasting in Python with ahead
  113. Did you ask ChatGPT about who you are?
  114. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  115. Simple interfaces to the forecasting API
  116. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  117. Prediction intervals (not only) for Boosted Configuration Networks in Python
  118. A Machine Learning workflow using Techtonique
  119. Super Mario Bros © in the browser using PyScript
  120. News from ESGtoolkit, ycinterextra, and nnetsauce
  121. Explaining a Keras _neural_ network predictions with the-teller
  122. New version of nnetsauce -- various quasi-randomized networks
  123. A dashboard illustrating bivariate time series forecasting with `ahead`
  124. Forecasting with `ahead` (Python version)
  125. Tuning and interpreting LSBoost
  126. Classification using linear regression
  127. Documentation and source code for GPopt, a package for Bayesian optimization
  128. Hyperparameters tuning with GPopt
  129. A forecasting tool (API) with examples in curl, R, Python
  130. Bayesian Optimization with GPopt Part 2 (save and resume)
  131. Bayesian Optimization with GPopt
  132. Compatibility of nnetsauce and mlsauce with scikit-learn
  133. Explaining xgboost predictions with the teller
  134. An infinity of time series models in nnetsauce
  135. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  136. A deeper learning architecture in nnetsauce
  137. Generalized nonlinear models in nnetsauce
  138. Boosting nonlinear penalized least squares
  139. Technical documentation
  140. A new version of nnetsauce, and a new Techtonique website
  141. Back next week, and a few announcements
  142. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  143. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  144. nnetsauce version 0.5.0, randomized neural networks on GPU
  145. Maximizing your tip as a waiter (Part 2)
  146. Parallel AdaOpt classification
  147. Maximizing your tip as a waiter
  148. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  149. AdaOpt
  150. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  151. Encoding your categorical variables based on the response variable and correlations
  152. Documentation for the querier, a query language for Data Frames
  153. Import data into the querier (now on Pypi), a query language for Data Frames
  154. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  155. A new version of nnetsauce (v0.3.1)
  156. Using R in Python for statistical learning/data science
  157. 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. Real-time pricing with a pretrained probabilistic stock return model
  2. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  3. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  4. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  5. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  6. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  7. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  8. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  9. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  10. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  11. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  12. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  13. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  14. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  15. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  16. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  17. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  18. R version of 'Backpropagating quasi-randomized neural networks'
  19. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  20. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  21. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  22. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  23. Free R, Python and SQL editors in techtonique dot net
  24. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  25. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  26. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  27. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  28. 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
  29. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  30. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  31. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  32. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  33. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  34. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  35. Survival stacking: survival analysis translated as supervised classification in R and Python
  36. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  37. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  38. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  39. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  40. Extending the Theta forecasting method to GLMs and attention
  41. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  42. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  43. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  44. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  45. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  46. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  47. CRAN-like repository for most recent releases of Techtonique's R packages
  48. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  49. Web app with DeepSeek R1 and Hugging Face API for chatting
  50. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  51. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  52. A simple test of the martingale hypothesis in esgtoolkit
  53. Command Line Interface (CLI) for techtonique.net's API
  54. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  55. 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
  56. Techtonique is out! (with a tutorial in various programming languages and formats)
  57. 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?
  58. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  59. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  60. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  61. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  62. GLMNet in Python: Generalized Linear Models
  63. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  64. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  65. Chat with your tabular data in www.techtonique.net
  66. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  67. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  68. R and Python consoles + JupyterLite in www.techtonique.net
  69. Gradient-Boosting anything (alert: high performance): Part2, R version
  70. Gradient-Boosting anything (alert: high performance)
  71. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  72. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  73. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  74. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  75. New versions of nnetsauce and ahead
  76. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  77. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  78. R package development workflow (assuming you're using macOS or Linux)
  79. A new method for deriving a nonparametric confidence interval for the mean
  80. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  81. Bayesian (nonlinear) adaptive learning
  82. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  83. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  84. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  85. Forecasting the Economy
  86. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  87. Probability of receiving a loan; using learningmachine
  88. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  89. Conformalized predictive simulations for univariate time series on more than 250 data sets
  90. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  91. Multiple examples of Machine Learning forecasting with ahead
  92. rtopy (v0.1.1): calling R functions in Python
  93. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  94. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  95. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  96. 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
  97. Diffusion models in Python with esgtoolkit (Part2)
  98. Diffusion models in Python with esgtoolkit
  99. Quasi-randomized nnetworks in Julia, Python and R
  100. A plethora of datasets at your fingertips
  101. A classifier that's very accurate (and deep)
  102. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  103. Version v0.14.0 of nnetsauce for R and Python
  104. A diffusion model: G2++
  105. Diffusion models in ESGtoolkit + announcements
  106. Risk-neutralize simulations
  107. Comparing cross-validation results using crossval_ml and boxplots
  108. Did you ask ChatGPT about who you are?
  109. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  110. Simple interfaces to the forecasting API
  111. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  112. Boosted Configuration (neural) Networks Pt. 2
  113. Boosted Configuration (_neural_) Networks for classification
  114. News from ESGtoolkit, ycinterextra, and nnetsauce
  115. New version of nnetsauce -- various quasi-randomized networks
  116. A dashboard illustrating bivariate time series forecasting with `ahead`
  117. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  118. Time series cross-validation using `crossvalidation` (Part 2)
  119. Fast and scalable forecasting with ahead::ridge2f
  120. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  121. Forecasting with `ahead`
  122. `crossvalidation` and random search for calibrating support vector machines
  123. parallel grid search cross-validation using `crossvalidation`
  124. `crossvalidation` on R-universe, plus a classification example
  125. A forecasting tool (API) with examples in curl, R, Python
  126. An infinity of time series models in nnetsauce
  127. New activation functions in mlsauce's LSBoost
  128. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  129. Classify penguins with nnetsauce's MultitaskClassifier
  130. Bayesian forecasting for uni/multivariate time series
  131. Boosting nonlinear penalized least squares
  132. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  133. Submitting R package to CRAN
  134. Simulation of dependent variables in ESGtoolkit
  135. Forecasting lung disease progression
  136. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  137. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  138. nnetsauce version 0.5.0, randomized neural networks on GPU
  139. Maximizing your tip as a waiter (Part 2)
  140. Maximizing your tip as a waiter
  141. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  142. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  143. Custom errors for cross-validation using crossval::crossval_ml
  144. Encoding your categorical variables based on the response variable and correlations
  145. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  146. Grid search cross-validation using crossval
  147. Time series cross-validation using crossval
  148. On model specification, identification, degrees of freedom and regularization
  149. R notebooks for nnetsauce
  150. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  151. Feedback forms for contributing
  152. nnetsauce for R
  153. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  154. Using R in Python for statistical learning/data science
  155. Model calibration with `crossval`
  156. crossval