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Blogroll | Top

List of posts by date of publication | Top

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

Categories | Top

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