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Blogroll | Top

List of posts by date of publication | Top

  1. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  2. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  3. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart Feb 10, 2025
  4. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  5. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  6. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  7. 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
  8. Techtonique is out! (with a tutorial in various programming languages and formats) Jan 14, 2025
  9. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  10. 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
  11. 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
  12. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  13. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  14. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  15. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  16. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  17. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  18. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  19. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  20. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  21. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  22. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  23. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  24. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  25. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  26. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  27. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  28. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  29. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  30. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  31. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  32. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  33. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  34. New versions of nnetsauce and ahead Sep 9, 2024
  35. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  36. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  37. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  38. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  39. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  40. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  41. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  42. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  43. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  44. learningmachine for Python (new version) Jul 15, 2024
  45. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  46. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  47. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  48. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  49. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  50. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  51. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  52. Forecasting the Economy May 27, 2024
  53. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  54. Probability of receiving a loan; using learningmachine May 12, 2024
  55. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  56. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  57. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  58. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  59. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  60. learningmachine v1.1.2: for Python Apr 1, 2024
  61. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  62. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  63. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  64. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  65. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  66. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  67. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  68. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  69. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  70. Subsampling continuous and discrete response variables Jan 22, 2024
  71. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  72. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  73. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  74. 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
  75. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  76. Diffusion models in Python with esgtoolkit Dec 11, 2023
  77. Julia packaging at the command line Dec 4, 2023
  78. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  79. A plethora of datasets at your fingertips Nov 20, 2023
  80. A classifier that's very accurate (and deep) Nov 12, 2023
  81. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  82. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  83. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  84. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  85. A diffusion model: G2++ Oct 9, 2023
  86. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  87. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  88. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  89. Forecasting in Python with ahead Sep 11, 2023
  90. Risk-neutralize simulations Sep 4, 2023
  91. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  92. Reminder Apr 30, 2023
  93. Did you ask ChatGPT about who you are? Apr 16, 2023
  94. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  95. Simple interfaces to the forecasting API Nov 23, 2022
  96. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  97. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  98. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  99. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  100. A Machine Learning workflow using Techtonique Jun 6, 2022
  101. Super Mario Bros © in the browser using PyScript May 8, 2022
  102. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  103. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  104. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  105. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  106. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  107. Forecasting with `ahead` (Python version) Dec 13, 2021
  108. Tuning and interpreting LSBoost Nov 15, 2021
  109. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  110. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  111. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  112. Forecasting with `ahead` Oct 15, 2021
  113. Classification using linear regression Sep 26, 2021
  114. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  115. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  116. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  117. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  118. Hyperparameters tuning with GPopt Jun 11, 2021
  119. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  120. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  121. Bayesian Optimization with GPopt Apr 16, 2021
  122. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  123. Explaining xgboost predictions with the teller Mar 12, 2021
  124. An infinity of time series models in nnetsauce Mar 6, 2021
  125. New activation functions in mlsauce's LSBoost Feb 12, 2021
  126. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  127. A deeper learning architecture in nnetsauce Dec 18, 2020
  128. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  129. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  130. Generalized nonlinear models in nnetsauce Nov 28, 2020
  131. Boosting nonlinear penalized least squares Nov 21, 2020
  132. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  133. NEWS Oct 30, 2020
  134. A glimpse into my PhD journey Oct 23, 2020
  135. Submitting R package to CRAN Oct 16, 2020
  136. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  137. Forecasting lung disease progression Oct 2, 2020
  138. New nnetsauce Sep 25, 2020
  139. Technical documentation Sep 18, 2020
  140. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  141. Back next week, and a few announcements Sep 4, 2020
  142. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  143. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  144. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  145. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  146. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  147. Announcements Jun 26, 2020
  148. Parallel AdaOpt classification Jun 19, 2020
  149. Comments section and other news Jun 12, 2020
  150. Maximizing your tip as a waiter Jun 5, 2020
  151. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  152. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  153. AdaOpt May 15, 2020
  154. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  155. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  156. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  157. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  158. Grid search cross-validation using crossval Apr 10, 2020
  159. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  160. Time series cross-validation using crossval Mar 27, 2020
  161. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  162. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  163. R notebooks for nnetsauce Mar 6, 2020
  164. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  165. Create a specific feed in your Jekyll blog Feb 21, 2020
  166. Git/Github for contributing to package development Feb 14, 2020
  167. Feedback forms for contributing Feb 7, 2020
  168. nnetsauce for R Jan 31, 2020
  169. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  170. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  171. Search bar, new year 2020 Jan 10, 2020
  172. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  173. Understanding model interactions with the `teller` Dec 13, 2019
  174. Using the `teller` on a classifier Dec 6, 2019
  175. Benchmarking the querier's verbs Nov 29, 2019
  176. Composing the querier's verbs for data wrangling Nov 22, 2019
  177. Comparing and explaining model predictions with the teller Nov 15, 2019
  178. Tests for the significance of marginal effects in the teller Nov 8, 2019
  179. Introducing the teller Nov 1, 2019
  180. Introducing the querier Oct 25, 2019
  181. Prediction intervals for nnetsauce models Oct 18, 2019
  182. Using R in Python for statistical learning/data science Oct 11, 2019
  183. Model calibration with `crossval` Oct 4, 2019
  184. Bagging in the nnetsauce Sep 25, 2019
  185. Adaboost learning with nnetsauce Sep 18, 2019
  186. Change in blog's presentation Sep 4, 2019
  187. nnetsauce on Pypi Jun 5, 2019
  188. More nnetsauce (examples of use) May 9, 2019
  189. nnetsauce Mar 13, 2019
  190. crossval Mar 13, 2019
  191. 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. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  2. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  3. Command Line Interface (CLI) for techtonique.net's API
  4. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  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? Part2: 1311 time series from the Tourism competition
  6. Techtonique is out! (with a tutorial in various programming languages and formats)
  7. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  8. 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?
  9. 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)
  10. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  11. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  12. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  13. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  14. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  15. GLMNet in Python: Generalized Linear Models
  16. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  17. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  18. Chat with your tabular data in www.techtonique.net
  19. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  20. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  21. R and Python consoles + JupyterLite in www.techtonique.net
  22. Gradient-Boosting anything (alert: high performance)
  23. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  24. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  25. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  26. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  27. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  28. New versions of nnetsauce and ahead
  29. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  30. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  31. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  32. Copulas for uncertainty quantification in time series forecasting
  33. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  34. learningmachine for Python (new version)
  35. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  36. 10 uncertainty quantification methods in nnetsauce forecasting
  37. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  38. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  39. Automated hyperparameter tuning using any conformalized surrogate
  40. Recognizing handwritten digits with Ridge2Classifier
  41. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  42. Probability of receiving a loan; using learningmachine
  43. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  44. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  45. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  46. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  47. learningmachine v1.1.2: for Python
  48. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  49. rtopy (v0.1.1): calling R functions in Python
  50. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  51. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  52. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  53. Tuning Machine Learning models with GPopt's new version Part 2
  54. Tuning Machine Learning models with GPopt's new version
  55. Subsampling continuous and discrete response variables
  56. DeepMTS, a Deep Learning Model for Multivariate Time Series
  57. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  58. 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
  59. Diffusion models in Python with esgtoolkit (Part2)
  60. Diffusion models in Python with esgtoolkit
  61. Quasi-randomized nnetworks in Julia, Python and R
  62. A plethora of datasets at your fingertips
  63. A classifier that's very accurate (and deep)
  64. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  65. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  66. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  67. Version v0.14.0 of nnetsauce for R and Python
  68. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  69. (News from) forecasting in Python with ahead (progress bars and plots)
  70. Forecasting in Python with ahead
  71. Did you ask ChatGPT about who you are?
  72. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  73. Simple interfaces to the forecasting API
  74. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  75. Prediction intervals (not only) for Boosted Configuration Networks in Python
  76. A Machine Learning workflow using Techtonique
  77. Super Mario Bros © in the browser using PyScript
  78. News from ESGtoolkit, ycinterextra, and nnetsauce
  79. Explaining a Keras _neural_ network predictions with the-teller
  80. New version of nnetsauce -- various quasi-randomized networks
  81. A dashboard illustrating bivariate time series forecasting with `ahead`
  82. Forecasting with `ahead` (Python version)
  83. Tuning and interpreting LSBoost
  84. Classification using linear regression
  85. Documentation and source code for GPopt, a package for Bayesian optimization
  86. Hyperparameters tuning with GPopt
  87. A forecasting tool (API) with examples in curl, R, Python
  88. Bayesian Optimization with GPopt Part 2 (save and resume)
  89. Bayesian Optimization with GPopt
  90. Compatibility of nnetsauce and mlsauce with scikit-learn
  91. Explaining xgboost predictions with the teller
  92. An infinity of time series models in nnetsauce
  93. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  94. A deeper learning architecture in nnetsauce
  95. Generalized nonlinear models in nnetsauce
  96. Boosting nonlinear penalized least squares
  97. Technical documentation
  98. A new version of nnetsauce, and a new Techtonique website
  99. Back next week, and a few announcements
  100. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  101. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  102. nnetsauce version 0.5.0, randomized neural networks on GPU
  103. Maximizing your tip as a waiter (Part 2)
  104. Parallel AdaOpt classification
  105. Maximizing your tip as a waiter
  106. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  107. AdaOpt
  108. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  109. Encoding your categorical variables based on the response variable and correlations
  110. Documentation for the querier, a query language for Data Frames
  111. Import data into the querier (now on Pypi), a query language for Data Frames
  112. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  113. A new version of nnetsauce (v0.3.1)
  114. Using R in Python for statistical learning/data science
  115. 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. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  2. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  3. A simple test of the martingale hypothesis in esgtoolkit
  4. Command Line Interface (CLI) for techtonique.net's API
  5. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  6. 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
  7. Techtonique is out! (with a tutorial in various programming languages and formats)
  8. 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?
  9. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  10. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  11. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  12. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  13. GLMNet in Python: Generalized Linear Models
  14. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  15. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  16. Chat with your tabular data in www.techtonique.net
  17. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  18. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  19. R and Python consoles + JupyterLite in www.techtonique.net
  20. Gradient-Boosting anything (alert: high performance): Part2, R version
  21. Gradient-Boosting anything (alert: high performance)
  22. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  23. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  24. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  25. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  26. New versions of nnetsauce and ahead
  27. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  28. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  29. R package development workflow (assuming you're using macOS or Linux)
  30. A new method for deriving a nonparametric confidence interval for the mean
  31. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  32. Bayesian (nonlinear) adaptive learning
  33. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  34. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  35. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  36. Forecasting the Economy
  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. Conformalized predictive simulations for univariate time series on more than 250 data sets
  41. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  42. Multiple examples of Machine Learning forecasting with ahead
  43. rtopy (v0.1.1): calling R functions in Python
  44. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  45. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  46. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  47. 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
  48. Diffusion models in Python with esgtoolkit (Part2)
  49. Diffusion models in Python with esgtoolkit
  50. Quasi-randomized nnetworks in Julia, Python and R
  51. A plethora of datasets at your fingertips
  52. A classifier that's very accurate (and deep)
  53. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  54. Version v0.14.0 of nnetsauce for R and Python
  55. A diffusion model: G2++
  56. Diffusion models in ESGtoolkit + announcements
  57. Risk-neutralize simulations
  58. Comparing cross-validation results using crossval_ml and boxplots
  59. Did you ask ChatGPT about who you are?
  60. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  61. Simple interfaces to the forecasting API
  62. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  63. Boosted Configuration (neural) Networks Pt. 2
  64. Boosted Configuration (_neural_) Networks for classification
  65. News from ESGtoolkit, ycinterextra, and nnetsauce
  66. New version of nnetsauce -- various quasi-randomized networks
  67. A dashboard illustrating bivariate time series forecasting with `ahead`
  68. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  69. Time series cross-validation using `crossvalidation` (Part 2)
  70. Fast and scalable forecasting with ahead::ridge2f
  71. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  72. Forecasting with `ahead`
  73. `crossvalidation` and random search for calibrating support vector machines
  74. parallel grid search cross-validation using `crossvalidation`
  75. `crossvalidation` on R-universe, plus a classification example
  76. A forecasting tool (API) with examples in curl, R, Python
  77. An infinity of time series models in nnetsauce
  78. New activation functions in mlsauce's LSBoost
  79. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  80. Classify penguins with nnetsauce's MultitaskClassifier
  81. Bayesian forecasting for uni/multivariate time series
  82. Boosting nonlinear penalized least squares
  83. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  84. Submitting R package to CRAN
  85. Simulation of dependent variables in ESGtoolkit
  86. Forecasting lung disease progression
  87. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  88. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  89. nnetsauce version 0.5.0, randomized neural networks on GPU
  90. Maximizing your tip as a waiter (Part 2)
  91. Maximizing your tip as a waiter
  92. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  93. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  94. Custom errors for cross-validation using crossval::crossval_ml
  95. Encoding your categorical variables based on the response variable and correlations
  96. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  97. Grid search cross-validation using crossval
  98. Time series cross-validation using crossval
  99. On model specification, identification, degrees of freedom and regularization
  100. R notebooks for nnetsauce
  101. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  102. Feedback forms for contributing
  103. nnetsauce for R
  104. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  105. Using R in Python for statistical learning/data science
  106. Model calibration with `crossval`
  107. crossval