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Blogroll | Top


List of posts by date of publication | Top

  1. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  2. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  3. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  4. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  5. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  6. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  7. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  8. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  9. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  10. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  11. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  12. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  13. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  14. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  15. R version of 'Backpropagating quasi-randomized neural networks'
  16. Backpropagating quasi-randomized neural networks
  17. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  18. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  19. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  20. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  21. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  22. Free R, Python and SQL editors in techtonique dot net
  23. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  24. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  25. R version of 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. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  29. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  30. 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
  31. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  32. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  33. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  34. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  35. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  36. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  37. Survival stacking: survival analysis translated as supervised classification in R and Python
  38. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  39. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  40. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  41. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  42. Extending the Theta forecasting method to GLMs and attention
  43. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  44. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  45. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  46. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  47. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  48. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  49. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  50. CRAN-like repository for most recent releases of Techtonique's R packages
  51. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  52. Web app with DeepSeek R1 and Hugging Face API for chatting
  53. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  54. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  55. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  56. A simple test of the martingale hypothesis in esgtoolkit
  57. Command Line Interface (CLI) for techtonique.net's API
  58. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  59. 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
  60. Techtonique is out! (with a tutorial in various programming languages and formats)
  61. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  62. 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?
  63. 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)
  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. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  67. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  68. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  69. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  70. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  71. GLMNet in Python: Generalized Linear Models
  72. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  73. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  74. Chat with your tabular data in www.techtonique.net
  75. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  76. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  77. R and Python consoles + JupyterLite in www.techtonique.net
  78. Gradient-Boosting anything (alert: high performance): Part2, R version
  79. Gradient-Boosting anything (alert: high performance)
  80. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  81. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  82. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  83. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  84. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  85. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  86. New versions of nnetsauce and ahead
  87. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  88. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  89. R package development workflow (assuming you're using macOS or Linux)
  90. A new method for deriving a nonparametric confidence interval for the mean
  91. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  92. Bayesian (nonlinear) adaptive learning
  93. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  94. Copulas for uncertainty quantification in time series forecasting
  95. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  96. learningmachine for Python (new version)
  97. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  98. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  99. 10 uncertainty quantification methods in nnetsauce forecasting
  100. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  101. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  102. Automated hyperparameter tuning using any conformalized surrogate
  103. Recognizing handwritten digits with Ridge2Classifier
  104. Forecasting the Economy
  105. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  106. Probability of receiving a loan; using learningmachine
  107. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  108. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  109. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  110. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  111. Conformalized predictive simulations for univariate time series on more than 250 data sets
  112. learningmachine v1.1.2: for Python
  113. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  114. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  115. Multiple examples of Machine Learning forecasting with ahead
  116. rtopy (v0.1.1): calling R functions in Python
  117. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  118. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  119. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  120. Tuning Machine Learning models with GPopt's new version Part 2
  121. Tuning Machine Learning models with GPopt's new version
  122. Subsampling continuous and discrete response variables
  123. DeepMTS, a Deep Learning Model for Multivariate Time Series
  124. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  125. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  126. 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
  127. Diffusion models in Python with esgtoolkit (Part2)
  128. Diffusion models in Python with esgtoolkit
  129. Julia packaging at the command line
  130. Quasi-randomized nnetworks in Julia, Python and R
  131. A plethora of datasets at your fingertips
  132. A classifier that's very accurate (and deep)
  133. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  134. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  135. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  136. Version v0.14.0 of nnetsauce for R and Python
  137. A diffusion model: G2++
  138. Diffusion models in ESGtoolkit + announcements
  139. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  140. (News from) forecasting in Python with ahead (progress bars and plots)
  141. Forecasting in Python with ahead
  142. Risk-neutralize simulations
  143. Comparing cross-validation results using crossval_ml and boxplots
  144. Reminder
  145. Did you ask ChatGPT about who you are?
  146. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  147. Simple interfaces to the forecasting API
  148. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  149. Prediction intervals (not only) for Boosted Configuration Networks in Python
  150. Boosted Configuration (neural) Networks Pt. 2
  151. Boosted Configuration (_neural_) Networks for classification
  152. A Machine Learning workflow using Techtonique
  153. Super Mario Bros © in the browser using PyScript
  154. News from ESGtoolkit, ycinterextra, and nnetsauce
  155. Explaining a Keras _neural_ network predictions with the-teller
  156. New version of nnetsauce -- various quasi-randomized networks
  157. A dashboard illustrating bivariate time series forecasting with `ahead`
  158. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  159. Forecasting with `ahead` (Python version)
  160. Tuning and interpreting LSBoost
  161. Time series cross-validation using `crossvalidation` (Part 2)
  162. Fast and scalable forecasting with ahead::ridge2f
  163. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  164. Forecasting with `ahead`
  165. Classification using linear regression
  166. `crossvalidation` and random search for calibrating support vector machines
  167. parallel grid search cross-validation using `crossvalidation`
  168. `crossvalidation` on R-universe, plus a classification example
  169. Documentation and source code for GPopt, a package for Bayesian optimization
  170. Hyperparameters tuning with GPopt
  171. A forecasting tool (API) with examples in curl, R, Python
  172. Bayesian Optimization with GPopt Part 2 (save and resume)
  173. Bayesian Optimization with GPopt
  174. Compatibility of nnetsauce and mlsauce with scikit-learn
  175. Explaining xgboost predictions with the teller
  176. An infinity of time series models in nnetsauce
  177. New activation functions in mlsauce's LSBoost
  178. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  179. A deeper learning architecture in nnetsauce
  180. Classify penguins with nnetsauce's MultitaskClassifier
  181. Bayesian forecasting for uni/multivariate time series
  182. Generalized nonlinear models in nnetsauce
  183. Boosting nonlinear penalized least squares
  184. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  185. NEWS
  186. A glimpse into my PhD journey
  187. Submitting R package to CRAN
  188. Simulation of dependent variables in ESGtoolkit
  189. Forecasting lung disease progression
  190. New nnetsauce
  191. Technical documentation
  192. A new version of nnetsauce, and a new Techtonique website
  193. Back next week, and a few announcements
  194. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  195. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  196. nnetsauce version 0.5.0, randomized neural networks on GPU
  197. Maximizing your tip as a waiter (Part 2)
  198. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  199. Announcements
  200. Parallel AdaOpt classification
  201. Comments section and other news
  202. Maximizing your tip as a waiter
  203. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  204. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  205. AdaOpt
  206. Custom errors for cross-validation using crossval::crossval_ml
  207. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  208. Encoding your categorical variables based on the response variable and correlations
  209. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  210. Grid search cross-validation using crossval
  211. Documentation for the querier, a query language for Data Frames
  212. Time series cross-validation using crossval
  213. On model specification, identification, degrees of freedom and regularization
  214. Import data into the querier (now on Pypi), a query language for Data Frames
  215. R notebooks for nnetsauce
  216. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  217. Create a specific feed in your Jekyll blog
  218. Git/Github for contributing to package development
  219. Feedback forms for contributing
  220. nnetsauce for R
  221. A new version of nnetsauce (v0.3.1)
  222. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  223. Search bar, new year 2020
  224. 2019 Recap, the nnetsauce, the teller and the querier
  225. Understanding model interactions with the `teller`
  226. Using the `teller` on a classifier
  227. Benchmarking the querier's verbs
  228. Composing the querier's verbs for data wrangling
  229. Comparing and explaining model predictions with the teller
  230. Tests for the significance of marginal effects in the teller
  231. Introducing the teller
  232. Introducing the querier
  233. Prediction intervals for nnetsauce models
  234. Using R in Python for statistical learning/data science
  235. Model calibration with `crossval`
  236. Bagging in the nnetsauce
  237. Adaboost learning with nnetsauce
  238. Change in blog's presentation
  239. nnetsauce on Pypi
  240. More nnetsauce (examples of use)
  241. nnetsauce
  242. crossval
  243. 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. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  2. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  3. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  4. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  5. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  6. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  7. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  8. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  9. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  10. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  11. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  12. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  13. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  14. Backpropagating quasi-randomized neural networks
  15. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  16. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  17. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  18. Free R, Python and SQL editors in techtonique dot net
  19. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  20. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  21. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  22. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  23. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  24. 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
  25. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  26. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  27. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  28. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  29. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  30. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  31. Survival stacking: survival analysis translated as supervised classification in R and Python
  32. Extending the Theta forecasting method to GLMs and attention
  33. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  34. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  35. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  36. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  37. Web app with DeepSeek R1 and Hugging Face API for chatting
  38. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  39. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  40. Command Line Interface (CLI) for techtonique.net's API
  41. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  42. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  43. Techtonique is out! (with a tutorial in various programming languages and formats)
  44. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  45. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  46. 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)
  47. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  48. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  49. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  50. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  51. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  52. GLMNet in Python: Generalized Linear Models
  53. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  54. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  55. Chat with your tabular data in www.techtonique.net
  56. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  57. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  58. R and Python consoles + JupyterLite in www.techtonique.net
  59. Gradient-Boosting anything (alert: high performance)
  60. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  61. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  62. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  63. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  64. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  65. New versions of nnetsauce and ahead
  66. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  67. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  68. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  69. Copulas for uncertainty quantification in time series forecasting
  70. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  71. learningmachine for Python (new version)
  72. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  73. 10 uncertainty quantification methods in nnetsauce forecasting
  74. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  75. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  76. Automated hyperparameter tuning using any conformalized surrogate
  77. Recognizing handwritten digits with Ridge2Classifier
  78. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  79. Probability of receiving a loan; using learningmachine
  80. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  81. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  82. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  83. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  84. learningmachine v1.1.2: for Python
  85. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  86. rtopy (v0.1.1): calling R functions in Python
  87. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  88. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  89. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  90. Tuning Machine Learning models with GPopt's new version Part 2
  91. Tuning Machine Learning models with GPopt's new version
  92. Subsampling continuous and discrete response variables
  93. DeepMTS, a Deep Learning Model for Multivariate Time Series
  94. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  95. 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
  96. Diffusion models in Python with esgtoolkit (Part2)
  97. Diffusion models in Python with esgtoolkit
  98. Quasi-randomized nnetworks in Julia, Python and R
  99. A plethora of datasets at your fingertips
  100. A classifier that's very accurate (and deep)
  101. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  102. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  103. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  104. Version v0.14.0 of nnetsauce for R and Python
  105. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  106. (News from) forecasting in Python with ahead (progress bars and plots)
  107. Forecasting in Python with ahead
  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. Prediction intervals (not only) for Boosted Configuration Networks in Python
  113. A Machine Learning workflow using Techtonique
  114. Super Mario Bros © in the browser using PyScript
  115. News from ESGtoolkit, ycinterextra, and nnetsauce
  116. Explaining a Keras _neural_ network predictions with the-teller
  117. New version of nnetsauce -- various quasi-randomized networks
  118. A dashboard illustrating bivariate time series forecasting with `ahead`
  119. Forecasting with `ahead` (Python version)
  120. Tuning and interpreting LSBoost
  121. Classification using linear regression
  122. Documentation and source code for GPopt, a package for Bayesian optimization
  123. Hyperparameters tuning with GPopt
  124. A forecasting tool (API) with examples in curl, R, Python
  125. Bayesian Optimization with GPopt Part 2 (save and resume)
  126. Bayesian Optimization with GPopt
  127. Compatibility of nnetsauce and mlsauce with scikit-learn
  128. Explaining xgboost predictions with the teller
  129. An infinity of time series models in nnetsauce
  130. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  131. A deeper learning architecture in nnetsauce
  132. Generalized nonlinear models in nnetsauce
  133. Boosting nonlinear penalized least squares
  134. Technical documentation
  135. A new version of nnetsauce, and a new Techtonique website
  136. Back next week, and a few announcements
  137. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  138. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  139. nnetsauce version 0.5.0, randomized neural networks on GPU
  140. Maximizing your tip as a waiter (Part 2)
  141. Parallel AdaOpt classification
  142. Maximizing your tip as a waiter
  143. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  144. AdaOpt
  145. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  146. Encoding your categorical variables based on the response variable and correlations
  147. Documentation for the querier, a query language for Data Frames
  148. Import data into the querier (now on Pypi), a query language for Data Frames
  149. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  150. A new version of nnetsauce (v0.3.1)
  151. Using R in Python for statistical learning/data science
  152. 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. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  2. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  3. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  4. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  5. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  6. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  7. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  8. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  9. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  10. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  11. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  12. R version of 'Backpropagating quasi-randomized neural networks'
  13. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  14. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  15. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  16. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  17. Free R, Python and SQL editors in techtonique dot net
  18. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  19. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  20. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  21. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  22. 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
  23. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  24. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  25. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  26. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  27. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  28. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  29. Survival stacking: survival analysis translated as supervised classification in R and Python
  30. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  31. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  32. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  33. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  34. Extending the Theta forecasting method to GLMs and attention
  35. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  36. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  37. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  38. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  39. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  40. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  41. CRAN-like repository for most recent releases of Techtonique's R packages
  42. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  43. Web app with DeepSeek R1 and Hugging Face API for chatting
  44. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  45. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  46. A simple test of the martingale hypothesis in esgtoolkit
  47. Command Line Interface (CLI) for techtonique.net's API
  48. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  49. 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
  50. Techtonique is out! (with a tutorial in various programming languages and formats)
  51. 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?
  52. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  53. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  54. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  55. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  56. GLMNet in Python: Generalized Linear Models
  57. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  58. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  59. Chat with your tabular data in www.techtonique.net
  60. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  61. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  62. R and Python consoles + JupyterLite in www.techtonique.net
  63. Gradient-Boosting anything (alert: high performance): Part2, R version
  64. Gradient-Boosting anything (alert: high performance)
  65. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  66. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  67. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  68. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  69. New versions of nnetsauce and ahead
  70. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  71. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  72. R package development workflow (assuming you're using macOS or Linux)
  73. A new method for deriving a nonparametric confidence interval for the mean
  74. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  75. Bayesian (nonlinear) adaptive learning
  76. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  77. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  78. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  79. Forecasting the Economy
  80. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  81. Probability of receiving a loan; using learningmachine
  82. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  83. Conformalized predictive simulations for univariate time series on more than 250 data sets
  84. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  85. Multiple examples of Machine Learning forecasting with ahead
  86. rtopy (v0.1.1): calling R functions in Python
  87. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  88. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  89. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  90. 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
  91. Diffusion models in Python with esgtoolkit (Part2)
  92. Diffusion models in Python with esgtoolkit
  93. Quasi-randomized nnetworks in Julia, Python and R
  94. A plethora of datasets at your fingertips
  95. A classifier that's very accurate (and deep)
  96. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  97. Version v0.14.0 of nnetsauce for R and Python
  98. A diffusion model: G2++
  99. Diffusion models in ESGtoolkit + announcements
  100. Risk-neutralize simulations
  101. Comparing cross-validation results using crossval_ml and boxplots
  102. Did you ask ChatGPT about who you are?
  103. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  104. Simple interfaces to the forecasting API
  105. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  106. Boosted Configuration (neural) Networks Pt. 2
  107. Boosted Configuration (_neural_) Networks for classification
  108. News from ESGtoolkit, ycinterextra, and nnetsauce
  109. New version of nnetsauce -- various quasi-randomized networks
  110. A dashboard illustrating bivariate time series forecasting with `ahead`
  111. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  112. Time series cross-validation using `crossvalidation` (Part 2)
  113. Fast and scalable forecasting with ahead::ridge2f
  114. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  115. Forecasting with `ahead`
  116. `crossvalidation` and random search for calibrating support vector machines
  117. parallel grid search cross-validation using `crossvalidation`
  118. `crossvalidation` on R-universe, plus a classification example
  119. A forecasting tool (API) with examples in curl, R, Python
  120. An infinity of time series models in nnetsauce
  121. New activation functions in mlsauce's LSBoost
  122. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  123. Classify penguins with nnetsauce's MultitaskClassifier
  124. Bayesian forecasting for uni/multivariate time series
  125. Boosting nonlinear penalized least squares
  126. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  127. Submitting R package to CRAN
  128. Simulation of dependent variables in ESGtoolkit
  129. Forecasting lung disease progression
  130. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  131. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  132. nnetsauce version 0.5.0, randomized neural networks on GPU
  133. Maximizing your tip as a waiter (Part 2)
  134. Maximizing your tip as a waiter
  135. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  136. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  137. Custom errors for cross-validation using crossval::crossval_ml
  138. Encoding your categorical variables based on the response variable and correlations
  139. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  140. Grid search cross-validation using crossval
  141. Time series cross-validation using crossval
  142. On model specification, identification, degrees of freedom and regularization
  143. R notebooks for nnetsauce
  144. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  145. Feedback forms for contributing
  146. nnetsauce for R
  147. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  148. Using R in Python for statistical learning/data science
  149. Model calibration with `crossval`
  150. crossval