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Blogroll | Top


List of posts by date of publication | Top

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