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Blogroll | Top


List of posts by date of publication | Top

  1. RPweave: Unified R + Python + LaTeX System
  2. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  3. Part2 of More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  4. More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  5. R port of llama2.c
  6. Native uncertainty quantification for time series with NGBoost
  7. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  8. Real-time pricing with a pretrained probabilistic stock return model
  9. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  10. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  11. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  12. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  13. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  14. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  15. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  16. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  17. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  18. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  19. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  20. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  21. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  22. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  23. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  24. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  25. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  26. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  27. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  28. R version of 'Backpropagating quasi-randomized neural networks'
  29. Backpropagating quasi-randomized neural networks
  30. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  31. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  32. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  33. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  34. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  35. Free R, Python and SQL editors in techtonique dot net
  36. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  37. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  38. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  39. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  40. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  41. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  42. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  43. 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
  44. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  45. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  46. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  47. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  48. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  49. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  50. Survival stacking: survival analysis translated as supervised classification in R and Python
  51. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  52. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  53. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  54. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  55. Extending the Theta forecasting method to GLMs and attention
  56. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  57. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  58. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  59. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  60. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  61. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  62. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  63. CRAN-like repository for most recent releases of Techtonique's R packages
  64. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  65. Web app with DeepSeek R1 and Hugging Face API for chatting
  66. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  67. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  68. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  69. A simple test of the martingale hypothesis in esgtoolkit
  70. Command Line Interface (CLI) for techtonique.net's API
  71. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  72. 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
  73. Techtonique is out! (with a tutorial in various programming languages and formats)
  74. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  75. 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?
  76. 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)
  77. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  78. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  79. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  80. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  81. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  82. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  83. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  84. GLMNet in Python: Generalized Linear Models
  85. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  86. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  87. Chat with your tabular data in www.techtonique.net
  88. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  89. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  90. R and Python consoles + JupyterLite in www.techtonique.net
  91. Gradient-Boosting anything (alert: high performance): Part2, R version
  92. Gradient-Boosting anything (alert: high performance)
  93. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  94. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  95. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  96. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  97. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  98. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  99. New versions of nnetsauce and ahead
  100. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  101. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  102. R package development workflow (assuming you're using macOS or Linux)
  103. A new method for deriving a nonparametric confidence interval for the mean
  104. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  105. Bayesian (nonlinear) adaptive learning
  106. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  107. Copulas for uncertainty quantification in time series forecasting
  108. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  109. learningmachine for Python (new version)
  110. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  111. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  112. 10 uncertainty quantification methods in nnetsauce forecasting
  113. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  114. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  115. Automated hyperparameter tuning using any conformalized surrogate
  116. Recognizing handwritten digits with Ridge2Classifier
  117. Forecasting the Economy
  118. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  119. Probability of receiving a loan; using learningmachine
  120. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  121. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  122. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  123. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  124. Conformalized predictive simulations for univariate time series on more than 250 data sets
  125. learningmachine v1.1.2: for Python
  126. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  127. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  128. Multiple examples of Machine Learning forecasting with ahead
  129. rtopy (v0.1.1): calling R functions in Python
  130. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  131. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  132. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  133. Tuning Machine Learning models with GPopt's new version Part 2
  134. Tuning Machine Learning models with GPopt's new version
  135. Subsampling continuous and discrete response variables
  136. DeepMTS, a Deep Learning Model for Multivariate Time Series
  137. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  138. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  139. 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
  140. Diffusion models in Python with esgtoolkit (Part2)
  141. Diffusion models in Python with esgtoolkit
  142. Julia packaging at the command line
  143. Quasi-randomized nnetworks in Julia, Python and R
  144. A plethora of datasets at your fingertips
  145. A classifier that's very accurate (and deep)
  146. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  147. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  148. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  149. Version v0.14.0 of nnetsauce for R and Python
  150. A diffusion model: G2++
  151. Diffusion models in ESGtoolkit + announcements
  152. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  153. (News from) forecasting in Python with ahead (progress bars and plots)
  154. Forecasting in Python with ahead
  155. Risk-neutralize simulations
  156. Comparing cross-validation results using crossval_ml and boxplots
  157. Reminder
  158. Did you ask ChatGPT about who you are?
  159. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  160. Simple interfaces to the forecasting API
  161. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  162. Prediction intervals (not only) for Boosted Configuration Networks in Python
  163. Boosted Configuration (neural) Networks Pt. 2
  164. Boosted Configuration (_neural_) Networks for classification
  165. A Machine Learning workflow using Techtonique
  166. Super Mario Bros © in the browser using PyScript
  167. News from ESGtoolkit, ycinterextra, and nnetsauce
  168. Explaining a Keras _neural_ network predictions with the-teller
  169. New version of nnetsauce -- various quasi-randomized networks
  170. A dashboard illustrating bivariate time series forecasting with `ahead`
  171. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  172. Forecasting with `ahead` (Python version)
  173. Tuning and interpreting LSBoost
  174. Time series cross-validation using `crossvalidation` (Part 2)
  175. Fast and scalable forecasting with ahead::ridge2f
  176. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  177. Forecasting with `ahead`
  178. Classification using linear regression
  179. `crossvalidation` and random search for calibrating support vector machines
  180. parallel grid search cross-validation using `crossvalidation`
  181. `crossvalidation` on R-universe, plus a classification example
  182. Documentation and source code for GPopt, a package for Bayesian optimization
  183. Hyperparameters tuning with GPopt
  184. A forecasting tool (API) with examples in curl, R, Python
  185. Bayesian Optimization with GPopt Part 2 (save and resume)
  186. Bayesian Optimization with GPopt
  187. Compatibility of nnetsauce and mlsauce with scikit-learn
  188. Explaining xgboost predictions with the teller
  189. An infinity of time series models in nnetsauce
  190. New activation functions in mlsauce's LSBoost
  191. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  192. A deeper learning architecture in nnetsauce
  193. Classify penguins with nnetsauce's MultitaskClassifier
  194. Bayesian forecasting for uni/multivariate time series
  195. Generalized nonlinear models in nnetsauce
  196. Boosting nonlinear penalized least squares
  197. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  198. NEWS
  199. A glimpse into my PhD journey
  200. Submitting R package to CRAN
  201. Simulation of dependent variables in ESGtoolkit
  202. Forecasting lung disease progression
  203. New nnetsauce
  204. Technical documentation
  205. A new version of nnetsauce, and a new Techtonique website
  206. Back next week, and a few announcements
  207. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  208. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  209. nnetsauce version 0.5.0, randomized neural networks on GPU
  210. Maximizing your tip as a waiter (Part 2)
  211. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  212. Announcements
  213. Parallel AdaOpt classification
  214. Comments section and other news
  215. Maximizing your tip as a waiter
  216. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  217. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  218. AdaOpt
  219. Custom errors for cross-validation using crossval::crossval_ml
  220. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  221. Encoding your categorical variables based on the response variable and correlations
  222. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  223. Grid search cross-validation using crossval
  224. Documentation for the querier, a query language for Data Frames
  225. Time series cross-validation using crossval
  226. On model specification, identification, degrees of freedom and regularization
  227. Import data into the querier (now on Pypi), a query language for Data Frames
  228. R notebooks for nnetsauce
  229. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  230. Create a specific feed in your Jekyll blog
  231. Git/Github for contributing to package development
  232. Feedback forms for contributing
  233. nnetsauce for R
  234. A new version of nnetsauce (v0.3.1)
  235. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  236. Search bar, new year 2020
  237. 2019 Recap, the nnetsauce, the teller and the querier
  238. Understanding model interactions with the `teller`
  239. Using the `teller` on a classifier
  240. Benchmarking the querier's verbs
  241. Composing the querier's verbs for data wrangling
  242. Comparing and explaining model predictions with the teller
  243. Tests for the significance of marginal effects in the teller
  244. Introducing the teller
  245. Introducing the querier
  246. Prediction intervals for nnetsauce models
  247. Using R in Python for statistical learning/data science
  248. Model calibration with `crossval`
  249. Bagging in the nnetsauce
  250. Adaboost learning with nnetsauce
  251. Change in blog's presentation
  252. nnetsauce on Pypi
  253. More nnetsauce (examples of use)
  254. nnetsauce
  255. crossval
  256. 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. RPweave: Unified R + Python + LaTeX System
  2. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  3. Part2 of More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  4. More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  5. Native uncertainty quantification for time series with NGBoost
  6. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  7. Real-time pricing with a pretrained probabilistic stock return model
  8. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  9. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  10. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  11. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  12. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  13. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  14. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  15. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  16. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  17. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  18. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  19. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  20. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  21. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  22. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  23. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  24. Backpropagating quasi-randomized neural networks
  25. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  26. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  27. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  28. Free R, Python and SQL editors in techtonique dot net
  29. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  30. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  31. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  32. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  33. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  34. 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
  35. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  36. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  37. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  38. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  39. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  40. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  41. Survival stacking: survival analysis translated as supervised classification in R and Python
  42. Extending the Theta forecasting method to GLMs and attention
  43. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  44. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  45. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  46. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  47. Web app with DeepSeek R1 and Hugging Face API for chatting
  48. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  49. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  50. Command Line Interface (CLI) for techtonique.net's API
  51. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  52. 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
  53. Techtonique is out! (with a tutorial in various programming languages and formats)
  54. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  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?
  56. 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)
  57. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  58. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  59. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  60. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  61. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  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)
  70. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  71. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  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. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  78. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  79. Copulas for uncertainty quantification in time series forecasting
  80. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  81. learningmachine for Python (new version)
  82. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  83. 10 uncertainty quantification methods in nnetsauce forecasting
  84. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  85. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  86. Automated hyperparameter tuning using any conformalized surrogate
  87. Recognizing handwritten digits with Ridge2Classifier
  88. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  89. Probability of receiving a loan; using learningmachine
  90. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  91. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  92. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  93. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  94. learningmachine v1.1.2: for Python
  95. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  96. rtopy (v0.1.1): calling R functions in Python
  97. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  98. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  99. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  100. Tuning Machine Learning models with GPopt's new version Part 2
  101. Tuning Machine Learning models with GPopt's new version
  102. Subsampling continuous and discrete response variables
  103. DeepMTS, a Deep Learning Model for Multivariate Time Series
  104. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  105. 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
  106. Diffusion models in Python with esgtoolkit (Part2)
  107. Diffusion models in Python with esgtoolkit
  108. Quasi-randomized nnetworks in Julia, Python and R
  109. A plethora of datasets at your fingertips
  110. A classifier that's very accurate (and deep)
  111. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  112. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  113. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  114. Version v0.14.0 of nnetsauce for R and Python
  115. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  116. (News from) forecasting in Python with ahead (progress bars and plots)
  117. Forecasting in Python with ahead
  118. Did you ask ChatGPT about who you are?
  119. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  120. Simple interfaces to the forecasting API
  121. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  122. Prediction intervals (not only) for Boosted Configuration Networks in Python
  123. A Machine Learning workflow using Techtonique
  124. Super Mario Bros © in the browser using PyScript
  125. News from ESGtoolkit, ycinterextra, and nnetsauce
  126. Explaining a Keras _neural_ network predictions with the-teller
  127. New version of nnetsauce -- various quasi-randomized networks
  128. A dashboard illustrating bivariate time series forecasting with `ahead`
  129. Forecasting with `ahead` (Python version)
  130. Tuning and interpreting LSBoost
  131. Classification using linear regression
  132. Documentation and source code for GPopt, a package for Bayesian optimization
  133. Hyperparameters tuning with GPopt
  134. A forecasting tool (API) with examples in curl, R, Python
  135. Bayesian Optimization with GPopt Part 2 (save and resume)
  136. Bayesian Optimization with GPopt
  137. Compatibility of nnetsauce and mlsauce with scikit-learn
  138. Explaining xgboost predictions with the teller
  139. An infinity of time series models in nnetsauce
  140. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  141. A deeper learning architecture in nnetsauce
  142. Generalized nonlinear models in nnetsauce
  143. Boosting nonlinear penalized least squares
  144. Technical documentation
  145. A new version of nnetsauce, and a new Techtonique website
  146. Back next week, and a few announcements
  147. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  148. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  149. nnetsauce version 0.5.0, randomized neural networks on GPU
  150. Maximizing your tip as a waiter (Part 2)
  151. Parallel AdaOpt classification
  152. Maximizing your tip as a waiter
  153. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  154. AdaOpt
  155. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  156. Encoding your categorical variables based on the response variable and correlations
  157. Documentation for the querier, a query language for Data Frames
  158. Import data into the querier (now on Pypi), a query language for Data Frames
  159. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  160. A new version of nnetsauce (v0.3.1)
  161. Using R in Python for statistical learning/data science
  162. 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. RPweave: Unified R + Python + LaTeX System
  2. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  3. Part2 of More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  4. More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  5. R port of llama2.c
  6. Native uncertainty quantification for time series with NGBoost
  7. Real-time pricing with a pretrained probabilistic stock return model
  8. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  9. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  10. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  11. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  12. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  13. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  14. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  15. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  16. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  17. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  18. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  19. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  20. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  21. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  22. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  23. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  24. R version of 'Backpropagating quasi-randomized neural networks'
  25. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  26. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  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. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  32. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  33. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  34. 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
  35. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  36. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  37. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  38. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  39. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  40. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  41. Survival stacking: survival analysis translated as supervised classification in R and Python
  42. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  43. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  44. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  45. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  46. Extending the Theta forecasting method to GLMs and attention
  47. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  48. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  49. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  50. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  51. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  52. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  53. CRAN-like repository for most recent releases of Techtonique's R packages
  54. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  55. Web app with DeepSeek R1 and Hugging Face API for chatting
  56. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  57. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  58. A simple test of the martingale hypothesis in esgtoolkit
  59. Command Line Interface (CLI) for techtonique.net's API
  60. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  61. 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
  62. Techtonique is out! (with a tutorial in various programming languages and formats)
  63. 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?
  64. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  65. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  66. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  67. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  68. GLMNet in Python: Generalized Linear Models
  69. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  70. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  71. Chat with your tabular data in www.techtonique.net
  72. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  73. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  74. R and Python consoles + JupyterLite in www.techtonique.net
  75. Gradient-Boosting anything (alert: high performance): Part2, R version
  76. Gradient-Boosting anything (alert: high performance)
  77. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  78. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  79. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  80. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  81. New versions of nnetsauce and ahead
  82. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  83. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  84. R package development workflow (assuming you're using macOS or Linux)
  85. A new method for deriving a nonparametric confidence interval for the mean
  86. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  87. Bayesian (nonlinear) adaptive learning
  88. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  89. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  90. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  91. Forecasting the Economy
  92. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  93. Probability of receiving a loan; using learningmachine
  94. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  95. Conformalized predictive simulations for univariate time series on more than 250 data sets
  96. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  97. Multiple examples of Machine Learning forecasting with ahead
  98. rtopy (v0.1.1): calling R functions in Python
  99. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  100. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  101. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  102. 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
  103. Diffusion models in Python with esgtoolkit (Part2)
  104. Diffusion models in Python with esgtoolkit
  105. Quasi-randomized nnetworks in Julia, Python and R
  106. A plethora of datasets at your fingertips
  107. A classifier that's very accurate (and deep)
  108. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  109. Version v0.14.0 of nnetsauce for R and Python
  110. A diffusion model: G2++
  111. Diffusion models in ESGtoolkit + announcements
  112. Risk-neutralize simulations
  113. Comparing cross-validation results using crossval_ml and boxplots
  114. Did you ask ChatGPT about who you are?
  115. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  116. Simple interfaces to the forecasting API
  117. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  118. Boosted Configuration (neural) Networks Pt. 2
  119. Boosted Configuration (_neural_) Networks for classification
  120. News from ESGtoolkit, ycinterextra, and nnetsauce
  121. New version of nnetsauce -- various quasi-randomized networks
  122. A dashboard illustrating bivariate time series forecasting with `ahead`
  123. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  124. Time series cross-validation using `crossvalidation` (Part 2)
  125. Fast and scalable forecasting with ahead::ridge2f
  126. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  127. Forecasting with `ahead`
  128. `crossvalidation` and random search for calibrating support vector machines
  129. parallel grid search cross-validation using `crossvalidation`
  130. `crossvalidation` on R-universe, plus a classification example
  131. A forecasting tool (API) with examples in curl, R, Python
  132. An infinity of time series models in nnetsauce
  133. New activation functions in mlsauce's LSBoost
  134. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  135. Classify penguins with nnetsauce's MultitaskClassifier
  136. Bayesian forecasting for uni/multivariate time series
  137. Boosting nonlinear penalized least squares
  138. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  139. Submitting R package to CRAN
  140. Simulation of dependent variables in ESGtoolkit
  141. Forecasting lung disease progression
  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. Maximizing your tip as a waiter
  147. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  148. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  149. Custom errors for cross-validation using crossval::crossval_ml
  150. Encoding your categorical variables based on the response variable and correlations
  151. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  152. Grid search cross-validation using crossval
  153. Time series cross-validation using crossval
  154. On model specification, identification, degrees of freedom and regularization
  155. R notebooks for nnetsauce
  156. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  157. Feedback forms for contributing
  158. nnetsauce for R
  159. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  160. Using R in Python for statistical learning/data science
  161. Model calibration with `crossval`
  162. crossval