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Blogroll | Top

List of posts by date of publication | Top

  1. 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 Jan 20, 2025
  2. Techtonique is out! (with a tutorial in various programming languages and formats) Jan 14, 2025
  3. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  4. 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? Jan 5, 2025
  5. 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) Dec 31, 2024
  6. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  7. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  8. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  9. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  10. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  11. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  12. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  13. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  14. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  15. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  16. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  17. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  18. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  19. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  20. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  21. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  22. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  23. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  24. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  25. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  26. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  27. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  28. New versions of nnetsauce and ahead Sep 9, 2024
  29. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  30. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  31. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  32. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  33. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  34. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  35. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  36. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  37. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  38. learningmachine for Python (new version) Jul 15, 2024
  39. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  40. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  41. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  42. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  43. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  44. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  45. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  46. Forecasting the Economy May 27, 2024
  47. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  48. Probability of receiving a loan; using learningmachine May 12, 2024
  49. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  50. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  51. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  52. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  53. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  54. learningmachine v1.1.2: for Python Apr 1, 2024
  55. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  56. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  57. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  58. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  59. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  60. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  61. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  62. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  63. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  64. Subsampling continuous and discrete response variables Jan 22, 2024
  65. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  66. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  67. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  68. 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 Dec 25, 2023
  69. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  70. Diffusion models in Python with esgtoolkit Dec 11, 2023
  71. Julia packaging at the command line Dec 4, 2023
  72. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  73. A plethora of datasets at your fingertips Nov 20, 2023
  74. A classifier that's very accurate (and deep) Nov 12, 2023
  75. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  76. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  77. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  78. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  79. A diffusion model: G2++ Oct 9, 2023
  80. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  81. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  82. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  83. Forecasting in Python with ahead Sep 11, 2023
  84. Risk-neutralize simulations Sep 4, 2023
  85. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  86. Reminder Apr 30, 2023
  87. Did you ask ChatGPT about who you are? Apr 16, 2023
  88. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  89. Simple interfaces to the forecasting API Nov 23, 2022
  90. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  91. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  92. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  93. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  94. A Machine Learning workflow using Techtonique Jun 6, 2022
  95. Super Mario Bros © in the browser using PyScript May 8, 2022
  96. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  97. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  98. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  99. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  100. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  101. Forecasting with `ahead` (Python version) Dec 13, 2021
  102. Tuning and interpreting LSBoost Nov 15, 2021
  103. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  104. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  105. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  106. Forecasting with `ahead` Oct 15, 2021
  107. Classification using linear regression Sep 26, 2021
  108. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  109. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  110. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  111. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  112. Hyperparameters tuning with GPopt Jun 11, 2021
  113. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  114. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  115. Bayesian Optimization with GPopt Apr 16, 2021
  116. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  117. Explaining xgboost predictions with the teller Mar 12, 2021
  118. An infinity of time series models in nnetsauce Mar 6, 2021
  119. New activation functions in mlsauce's LSBoost Feb 12, 2021
  120. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  121. A deeper learning architecture in nnetsauce Dec 18, 2020
  122. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  123. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  124. Generalized nonlinear models in nnetsauce Nov 28, 2020
  125. Boosting nonlinear penalized least squares Nov 21, 2020
  126. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  127. NEWS Oct 30, 2020
  128. A glimpse into my PhD journey Oct 23, 2020
  129. Submitting R package to CRAN Oct 16, 2020
  130. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  131. Forecasting lung disease progression Oct 2, 2020
  132. New nnetsauce Sep 25, 2020
  133. Technical documentation Sep 18, 2020
  134. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  135. Back next week, and a few announcements Sep 4, 2020
  136. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  137. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  138. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  139. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  140. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  141. Announcements Jun 26, 2020
  142. Parallel AdaOpt classification Jun 19, 2020
  143. Comments section and other news Jun 12, 2020
  144. Maximizing your tip as a waiter Jun 5, 2020
  145. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  146. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  147. AdaOpt May 15, 2020
  148. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  149. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  150. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  151. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  152. Grid search cross-validation using crossval Apr 10, 2020
  153. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  154. Time series cross-validation using crossval Mar 27, 2020
  155. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  156. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  157. R notebooks for nnetsauce Mar 6, 2020
  158. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  159. Create a specific feed in your Jekyll blog Feb 21, 2020
  160. Git/Github for contributing to package development Feb 14, 2020
  161. Feedback forms for contributing Feb 7, 2020
  162. nnetsauce for R Jan 31, 2020
  163. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  164. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  165. Search bar, new year 2020 Jan 10, 2020
  166. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  167. Understanding model interactions with the `teller` Dec 13, 2019
  168. Using the `teller` on a classifier Dec 6, 2019
  169. Benchmarking the querier's verbs Nov 29, 2019
  170. Composing the querier's verbs for data wrangling Nov 22, 2019
  171. Comparing and explaining model predictions with the teller Nov 15, 2019
  172. Tests for the significance of marginal effects in the teller Nov 8, 2019
  173. Introducing the teller Nov 1, 2019
  174. Introducing the querier Oct 25, 2019
  175. Prediction intervals for nnetsauce models Oct 18, 2019
  176. Using R in Python for statistical learning/data science Oct 11, 2019
  177. Model calibration with `crossval` Oct 4, 2019
  178. Bagging in the nnetsauce Sep 25, 2019
  179. Adaboost learning with nnetsauce Sep 18, 2019
  180. Change in blog's presentation Sep 4, 2019
  181. nnetsauce on Pypi Jun 5, 2019
  182. More nnetsauce (examples of use) May 9, 2019
  183. nnetsauce Mar 13, 2019
  184. crossval Mar 13, 2019
  185. test Mar 10, 2019

Categories | Top

Forecasting

  1. 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
  2. Techtonique is out! (with a tutorial in various programming languages and formats)
  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?
  4. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  5. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  6. 10 uncertainty quantification methods in nnetsauce forecasting
  7. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  8. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  9. DeepMTS, a Deep Learning Model for Multivariate Time Series
  10. Version v0.14.0 of nnetsauce for R and Python
  11. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  12. (News from) forecasting in Python with ahead (progress bars and plots)
  13. Forecasting in Python with ahead
  14. Simple interfaces to the forecasting API
  15. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  16. A dashboard illustrating bivariate time series forecasting with `ahead`
  17. 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. 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
  2. Techtonique is out! (with a tutorial in various programming languages and formats)
  3. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  4. 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?
  5. 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)
  6. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  7. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  8. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  9. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  10. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  11. GLMNet in Python: Generalized Linear Models
  12. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  13. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  14. Chat with your tabular data in www.techtonique.net
  15. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  16. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  17. R and Python consoles + JupyterLite in www.techtonique.net
  18. Gradient-Boosting anything (alert: high performance)
  19. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  20. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  21. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  22. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  23. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  24. New versions of nnetsauce and ahead
  25. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  26. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  27. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  28. Copulas for uncertainty quantification in time series forecasting
  29. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  30. learningmachine for Python (new version)
  31. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  32. 10 uncertainty quantification methods in nnetsauce forecasting
  33. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  34. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  35. Automated hyperparameter tuning using any conformalized surrogate
  36. Recognizing handwritten digits with Ridge2Classifier
  37. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  38. Probability of receiving a loan; using learningmachine
  39. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  40. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  41. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  42. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  43. learningmachine v1.1.2: for Python
  44. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  45. rtopy (v0.1.1): calling R functions in Python
  46. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  47. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  48. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  49. Tuning Machine Learning models with GPopt's new version Part 2
  50. Tuning Machine Learning models with GPopt's new version
  51. Subsampling continuous and discrete response variables
  52. DeepMTS, a Deep Learning Model for Multivariate Time Series
  53. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  54. 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
  55. Diffusion models in Python with esgtoolkit (Part2)
  56. Diffusion models in Python with esgtoolkit
  57. Quasi-randomized nnetworks in Julia, Python and R
  58. A plethora of datasets at your fingertips
  59. A classifier that's very accurate (and deep)
  60. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  61. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  62. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  63. Version v0.14.0 of nnetsauce for R and Python
  64. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  65. (News from) forecasting in Python with ahead (progress bars and plots)
  66. Forecasting in Python with ahead
  67. Did you ask ChatGPT about who you are?
  68. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  69. Simple interfaces to the forecasting API
  70. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  71. Prediction intervals (not only) for Boosted Configuration Networks in Python
  72. A Machine Learning workflow using Techtonique
  73. Super Mario Bros © in the browser using PyScript
  74. News from ESGtoolkit, ycinterextra, and nnetsauce
  75. Explaining a Keras _neural_ network predictions with the-teller
  76. New version of nnetsauce -- various quasi-randomized networks
  77. A dashboard illustrating bivariate time series forecasting with `ahead`
  78. Forecasting with `ahead` (Python version)
  79. Tuning and interpreting LSBoost
  80. Classification using linear regression
  81. Documentation and source code for GPopt, a package for Bayesian optimization
  82. Hyperparameters tuning with GPopt
  83. A forecasting tool (API) with examples in curl, R, Python
  84. Bayesian Optimization with GPopt Part 2 (save and resume)
  85. Bayesian Optimization with GPopt
  86. Compatibility of nnetsauce and mlsauce with scikit-learn
  87. Explaining xgboost predictions with the teller
  88. An infinity of time series models in nnetsauce
  89. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  90. A deeper learning architecture in nnetsauce
  91. Generalized nonlinear models in nnetsauce
  92. Boosting nonlinear penalized least squares
  93. Technical documentation
  94. A new version of nnetsauce, and a new Techtonique website
  95. Back next week, and a few announcements
  96. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  97. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  98. nnetsauce version 0.5.0, randomized neural networks on GPU
  99. Maximizing your tip as a waiter (Part 2)
  100. Parallel AdaOpt classification
  101. Maximizing your tip as a waiter
  102. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  103. AdaOpt
  104. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  105. Encoding your categorical variables based on the response variable and correlations
  106. Documentation for the querier, a query language for Data Frames
  107. Import data into the querier (now on Pypi), a query language for Data Frames
  108. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  109. A new version of nnetsauce (v0.3.1)
  110. Using R in Python for statistical learning/data science
  111. 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. 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
  2. Techtonique is out! (with a tutorial in various programming languages and formats)
  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?
  4. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  5. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  6. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  7. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  8. GLMNet in Python: Generalized Linear Models
  9. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  10. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  11. Chat with your tabular data in www.techtonique.net
  12. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  13. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  14. R and Python consoles + JupyterLite in www.techtonique.net
  15. Gradient-Boosting anything (alert: high performance): Part2, R version
  16. Gradient-Boosting anything (alert: high performance)
  17. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  18. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  19. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  20. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  21. New versions of nnetsauce and ahead
  22. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  23. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  24. R package development workflow (assuming you're using macOS or Linux)
  25. A new method for deriving a nonparametric confidence interval for the mean
  26. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  27. Bayesian (nonlinear) adaptive learning
  28. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  29. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  30. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  31. Forecasting the Economy
  32. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  33. Probability of receiving a loan; using learningmachine
  34. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  35. Conformalized predictive simulations for univariate time series on more than 250 data sets
  36. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  37. Multiple examples of Machine Learning forecasting with ahead
  38. rtopy (v0.1.1): calling R functions in Python
  39. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  40. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  41. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  42. 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
  43. Diffusion models in Python with esgtoolkit (Part2)
  44. Diffusion models in Python with esgtoolkit
  45. Quasi-randomized nnetworks in Julia, Python and R
  46. A plethora of datasets at your fingertips
  47. A classifier that's very accurate (and deep)
  48. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  49. Version v0.14.0 of nnetsauce for R and Python
  50. A diffusion model: G2++
  51. Diffusion models in ESGtoolkit + announcements
  52. Risk-neutralize simulations
  53. Comparing cross-validation results using crossval_ml and boxplots
  54. Did you ask ChatGPT about who you are?
  55. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  56. Simple interfaces to the forecasting API
  57. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  58. Boosted Configuration (neural) Networks Pt. 2
  59. Boosted Configuration (_neural_) Networks for classification
  60. News from ESGtoolkit, ycinterextra, and nnetsauce
  61. New version of nnetsauce -- various quasi-randomized networks
  62. A dashboard illustrating bivariate time series forecasting with `ahead`
  63. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  64. Time series cross-validation using `crossvalidation` (Part 2)
  65. Fast and scalable forecasting with ahead::ridge2f
  66. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  67. Forecasting with `ahead`
  68. `crossvalidation` and random search for calibrating support vector machines
  69. parallel grid search cross-validation using `crossvalidation`
  70. `crossvalidation` on R-universe, plus a classification example
  71. A forecasting tool (API) with examples in curl, R, Python
  72. An infinity of time series models in nnetsauce
  73. New activation functions in mlsauce's LSBoost
  74. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  75. Classify penguins with nnetsauce's MultitaskClassifier
  76. Bayesian forecasting for uni/multivariate time series
  77. Boosting nonlinear penalized least squares
  78. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  79. Submitting R package to CRAN
  80. Simulation of dependent variables in ESGtoolkit
  81. Forecasting lung disease progression
  82. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  83. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  84. nnetsauce version 0.5.0, randomized neural networks on GPU
  85. Maximizing your tip as a waiter (Part 2)
  86. Maximizing your tip as a waiter
  87. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  88. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  89. Custom errors for cross-validation using crossval::crossval_ml
  90. Encoding your categorical variables based on the response variable and correlations
  91. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  92. Grid search cross-validation using crossval
  93. Time series cross-validation using crossval
  94. On model specification, identification, degrees of freedom and regularization
  95. R notebooks for nnetsauce
  96. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  97. Feedback forms for contributing
  98. nnetsauce for R
  99. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  100. Using R in Python for statistical learning/data science
  101. Model calibration with `crossval`
  102. crossval