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Blogroll | Top


List of posts by date of publication | Top

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

QuasiRandomizedNN

  1. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  2. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  3. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  4. 10 uncertainty quantification methods in nnetsauce forecasting
  5. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  6. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  7. Automated hyperparameter tuning using any conformalized surrogate
  8. Recognizing handwritten digits with Ridge2Classifier
  9. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  10. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  11. DeepMTS, a Deep Learning Model for Multivariate Time Series
  12. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  13. Quasi-randomized nnetworks in Julia, Python and R
  14. A classifier that's very accurate (and deep)
  15. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  16. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  17. Version v0.14.0 of nnetsauce for R and Python
  18. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  19. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  20. New version of nnetsauce -- various quasi-randomized networks
  21. Tuning and interpreting LSBoost
  22. Classification using linear regression
  23. An infinity of time series models in nnetsauce
  24. A deeper learning architecture in nnetsauce
  25. Classify penguins with nnetsauce's MultitaskClassifier
  26. Bayesian forecasting for uni/multivariate time series
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. nnetsauce version 0.5.0, randomized neural networks on GPU
  32. R notebooks for nnetsauce
  33. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  34. Feedback forms for contributing
  35. nnetsauce for R
  36. A new version of nnetsauce (v0.3.1)
  37. 2019 Recap, the nnetsauce, the teller and the querier
  38. Prediction intervals for nnetsauce models
  39. Bagging in the nnetsauce
  40. Adaboost learning with nnetsauce
  41. nnetsauce on Pypi
  42. More nnetsauce (examples of use)
  43. nnetsauce

R

  1. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  2. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  3. Free R, Python and SQL editors in techtonique dot net
  4. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  5. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  6. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  7. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  8. 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
  9. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  10. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  11. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  12. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  13. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  14. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  15. Survival stacking: survival analysis translated as supervised classification in R and Python
  16. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  17. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  18. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  19. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  20. Extending the Theta forecasting method to GLMs and attention
  21. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  22. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  23. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  24. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  25. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  26. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  27. CRAN-like repository for most recent releases of Techtonique's R packages
  28. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  29. Web app with DeepSeek R1 and Hugging Face API for chatting
  30. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  31. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  32. A simple test of the martingale hypothesis in esgtoolkit
  33. Command Line Interface (CLI) for techtonique.net's API
  34. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  35. 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
  36. Techtonique is out! (with a tutorial in various programming languages and formats)
  37. 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?
  38. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  39. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  40. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  41. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  42. GLMNet in Python: Generalized Linear Models
  43. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  44. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  45. Chat with your tabular data in www.techtonique.net
  46. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  47. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  48. R and Python consoles + JupyterLite in www.techtonique.net
  49. Gradient-Boosting anything (alert: high performance): Part2, R version
  50. Gradient-Boosting anything (alert: high performance)
  51. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  52. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  53. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  54. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  55. New versions of nnetsauce and ahead
  56. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  57. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  58. R package development workflow (assuming you're using macOS or Linux)
  59. A new method for deriving a nonparametric confidence interval for the mean
  60. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  61. Bayesian (nonlinear) adaptive learning
  62. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  63. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  64. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  65. Forecasting the Economy
  66. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  67. Probability of receiving a loan; using learningmachine
  68. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  69. Conformalized predictive simulations for univariate time series on more than 250 data sets
  70. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  71. Multiple examples of Machine Learning forecasting with ahead
  72. rtopy (v0.1.1): calling R functions in Python
  73. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  74. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  75. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  76. 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
  77. Diffusion models in Python with esgtoolkit (Part2)
  78. Diffusion models in Python with esgtoolkit
  79. Quasi-randomized nnetworks in Julia, Python and R
  80. A plethora of datasets at your fingertips
  81. A classifier that's very accurate (and deep)
  82. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  83. Version v0.14.0 of nnetsauce for R and Python
  84. A diffusion model: G2++
  85. Diffusion models in ESGtoolkit + announcements
  86. Risk-neutralize simulations
  87. Comparing cross-validation results using crossval_ml and boxplots
  88. Did you ask ChatGPT about who you are?
  89. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  90. Simple interfaces to the forecasting API
  91. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  92. Boosted Configuration (neural) Networks Pt. 2
  93. Boosted Configuration (_neural_) Networks for classification
  94. News from ESGtoolkit, ycinterextra, and nnetsauce
  95. New version of nnetsauce -- various quasi-randomized networks
  96. A dashboard illustrating bivariate time series forecasting with `ahead`
  97. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  98. Time series cross-validation using `crossvalidation` (Part 2)
  99. Fast and scalable forecasting with ahead::ridge2f
  100. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  101. Forecasting with `ahead`
  102. `crossvalidation` and random search for calibrating support vector machines
  103. parallel grid search cross-validation using `crossvalidation`
  104. `crossvalidation` on R-universe, plus a classification example
  105. A forecasting tool (API) with examples in curl, R, Python
  106. An infinity of time series models in nnetsauce
  107. New activation functions in mlsauce's LSBoost
  108. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  109. Classify penguins with nnetsauce's MultitaskClassifier
  110. Bayesian forecasting for uni/multivariate time series
  111. Boosting nonlinear penalized least squares
  112. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  113. Submitting R package to CRAN
  114. Simulation of dependent variables in ESGtoolkit
  115. Forecasting lung disease progression
  116. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  117. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  118. nnetsauce version 0.5.0, randomized neural networks on GPU
  119. Maximizing your tip as a waiter (Part 2)
  120. Maximizing your tip as a waiter
  121. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  122. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  123. Custom errors for cross-validation using crossval::crossval_ml
  124. Encoding your categorical variables based on the response variable and correlations
  125. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  126. Grid search cross-validation using crossval
  127. Time series cross-validation using crossval
  128. On model specification, identification, degrees of freedom and regularization
  129. R notebooks for nnetsauce
  130. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  131. Feedback forms for contributing
  132. nnetsauce for R
  133. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  134. Using R in Python for statistical learning/data science
  135. Model calibration with `crossval`
  136. crossval