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Blogroll | Top


List of posts by date of publication | Top

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