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Blogroll | Top

List of posts by date of publication | Top

  1. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  2. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  3. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  4. learningmachine v1.1.2: for Python Apr 1, 2024
  5. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  6. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  7. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  8. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  9. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  10. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  11. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  12. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  13. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  14. Subsampling continuous and discrete response variables Jan 22, 2024
  15. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  16. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  17. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  18. 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
  19. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  20. Diffusion models in Python with esgtoolkit Dec 11, 2023
  21. Julia packaging at the command line Dec 4, 2023
  22. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  23. A plethora of datasets at your fingertips Nov 20, 2023
  24. A classifier that's very accurate (and deep) Nov 12, 2023
  25. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  26. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  27. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  28. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  29. A diffusion model: G2++ Oct 9, 2023
  30. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  31. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  32. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  33. Forecasting in Python with ahead Sep 11, 2023
  34. Risk-neutralize simulations Sep 4, 2023
  35. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  36. Reminder Apr 30, 2023
  37. Did you ask ChatGPT about who you are? Apr 16, 2023
  38. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  39. Simple interfaces to the forecasting API Nov 23, 2022
  40. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  41. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  42. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  43. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  44. A Machine Learning workflow using Techtonique Jun 6, 2022
  45. Super Mario Bros © in the browser using PyScript May 8, 2022
  46. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  47. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  48. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  49. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  50. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  51. Forecasting with `ahead` (Python version) Dec 13, 2021
  52. Tuning and interpreting LSBoost Nov 15, 2021
  53. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  54. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  55. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  56. Forecasting with `ahead` Oct 15, 2021
  57. Classification using linear regression Sep 26, 2021
  58. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  59. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  60. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  61. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  62. Hyperparameters tuning with GPopt Jun 11, 2021
  63. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  64. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  65. Bayesian Optimization with GPopt Apr 16, 2021
  66. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  67. Explaining xgboost predictions with the teller Mar 12, 2021
  68. An infinity of time series models in nnetsauce Mar 6, 2021
  69. New activation functions in mlsauce's LSBoost Feb 12, 2021
  70. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  71. A deeper learning architecture in nnetsauce Dec 18, 2020
  72. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  73. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  74. Generalized nonlinear models in nnetsauce Nov 28, 2020
  75. Boosting nonlinear penalized least squares Nov 21, 2020
  76. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  77. NEWS Oct 30, 2020
  78. A glimpse into my PhD journey Oct 23, 2020
  79. Submitting R package to CRAN Oct 16, 2020
  80. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  81. Forecasting lung disease progression Oct 2, 2020
  82. New nnetsauce Sep 25, 2020
  83. Technical documentation Sep 18, 2020
  84. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  85. Back next week, and a few announcements Sep 4, 2020
  86. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  87. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  88. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  89. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  90. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  91. Announcements Jun 26, 2020
  92. Parallel AdaOpt classification Jun 19, 2020
  93. Comments section and other news Jun 12, 2020
  94. Maximizing your tip as a waiter Jun 5, 2020
  95. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  96. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  97. AdaOpt May 15, 2020
  98. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  99. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  100. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  101. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  102. Grid search cross-validation using crossval Apr 10, 2020
  103. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  104. Time series cross-validation using crossval Mar 27, 2020
  105. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  106. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  107. R notebooks for nnetsauce Mar 6, 2020
  108. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  109. Create a specific feed in your Jekyll blog Feb 21, 2020
  110. Git/Github for contributing to package development Feb 14, 2020
  111. Feedback forms for contributing Feb 7, 2020
  112. nnetsauce for R Jan 31, 2020
  113. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  114. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  115. Search bar, new year 2020 Jan 10, 2020
  116. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  117. Understanding model interactions with the `teller` Dec 13, 2019
  118. Using the `teller` on a classifier Dec 6, 2019
  119. Benchmarking the querier's verbs Nov 29, 2019
  120. Composing the querier's verbs for data wrangling Nov 22, 2019
  121. Comparing and explaining model predictions with the teller Nov 15, 2019
  122. Tests for the significance of marginal effects in the teller Nov 8, 2019
  123. Introducing the teller Nov 1, 2019
  124. Introducing the querier Oct 25, 2019
  125. Prediction intervals for nnetsauce models Oct 18, 2019
  126. Using R in Python for statistical learning/data science Oct 11, 2019
  127. Model calibration with `crossval` Oct 4, 2019
  128. Bagging in the nnetsauce Sep 25, 2019
  129. Adaboost learning with nnetsauce Sep 18, 2019
  130. Change in blog's presentation Sep 4, 2019
  131. nnetsauce on Pypi Jun 5, 2019
  132. More nnetsauce (examples of use) May 9, 2019
  133. nnetsauce Mar 13, 2019
  134. crossval Mar 13, 2019
  135. 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. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  2. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  3. learningmachine v1.1.2: for Python
  4. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  5. rtopy (v0.1.1): calling R functions in Python
  6. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  7. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  8. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  9. Tuning Machine Learning models with GPopt's new version Part 2
  10. Tuning Machine Learning models with GPopt's new version
  11. Subsampling continuous and discrete response variables
  12. DeepMTS, a Deep Learning Model for Multivariate Time Series
  13. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  14. 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
  15. Diffusion models in Python with esgtoolkit (Part2)
  16. Diffusion models in Python with esgtoolkit
  17. Quasi-randomized nnetworks in Julia, Python and R
  18. A plethora of datasets at your fingertips
  19. A classifier that's very accurate (and deep)
  20. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  21. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  22. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  23. Version v0.14.0 of nnetsauce for R and Python
  24. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  25. (News from) forecasting in Python with ahead (progress bars and plots)
  26. Forecasting in Python with ahead
  27. Did you ask ChatGPT about who you are?
  28. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  29. Simple interfaces to the forecasting API
  30. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  31. Prediction intervals (not only) for Boosted Configuration Networks in Python
  32. A Machine Learning workflow using Techtonique
  33. Super Mario Bros © in the browser using PyScript
  34. News from ESGtoolkit, ycinterextra, and nnetsauce
  35. Explaining a Keras _neural_ network predictions with the-teller
  36. New version of nnetsauce -- various quasi-randomized networks
  37. A dashboard illustrating bivariate time series forecasting with `ahead`
  38. Forecasting with `ahead` (Python version)
  39. Tuning and interpreting LSBoost
  40. Classification using linear regression
  41. Documentation and source code for GPopt, a package for Bayesian optimization
  42. Hyperparameters tuning with GPopt
  43. A forecasting tool (API) with examples in curl, R, Python
  44. Bayesian Optimization with GPopt Part 2 (save and resume)
  45. Bayesian Optimization with GPopt
  46. Compatibility of nnetsauce and mlsauce with scikit-learn
  47. Explaining xgboost predictions with the teller
  48. An infinity of time series models in nnetsauce
  49. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  50. A deeper learning architecture in nnetsauce
  51. Generalized nonlinear models in nnetsauce
  52. Boosting nonlinear penalized least squares
  53. Technical documentation
  54. A new version of nnetsauce, and a new Techtonique website
  55. Back next week, and a few announcements
  56. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  57. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  58. nnetsauce version 0.5.0, randomized neural networks on GPU
  59. Maximizing your tip as a waiter (Part 2)
  60. Parallel AdaOpt classification
  61. Maximizing your tip as a waiter
  62. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  63. AdaOpt
  64. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  65. Encoding your categorical variables based on the response variable and correlations
  66. Documentation for the querier, a query language for Data Frames
  67. Import data into the querier (now on Pypi), a query language for Data Frames
  68. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  69. A new version of nnetsauce (v0.3.1)
  70. Using R in Python for statistical learning/data science
  71. nnetsauce on Pypi

QuasiRandomizedNN

  1. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  2. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  3. DeepMTS, a Deep Learning Model for Multivariate Time Series
  4. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  5. Quasi-randomized nnetworks in Julia, Python and R
  6. A classifier that's very accurate (and deep)
  7. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  8. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  9. Version v0.14.0 of nnetsauce for R and Python
  10. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  11. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  12. New version of nnetsauce -- various quasi-randomized networks
  13. Tuning and interpreting LSBoost
  14. Classification using linear regression
  15. An infinity of time series models in nnetsauce
  16. A deeper learning architecture in nnetsauce
  17. Classify penguins with nnetsauce's MultitaskClassifier
  18. Bayesian forecasting for uni/multivariate time series
  19. New nnetsauce
  20. Technical documentation
  21. A new version of nnetsauce, and a new Techtonique website
  22. Back next week, and a few announcements
  23. nnetsauce version 0.5.0, randomized neural networks on GPU
  24. R notebooks for nnetsauce
  25. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  26. Feedback forms for contributing
  27. nnetsauce for R
  28. A new version of nnetsauce (v0.3.1)
  29. 2019 Recap, the nnetsauce, the teller and the querier
  30. Prediction intervals for nnetsauce models
  31. Bagging in the nnetsauce
  32. Adaboost learning with nnetsauce
  33. nnetsauce on Pypi
  34. More nnetsauce (examples of use)
  35. nnetsauce

R

  1. Conformalized predictive simulations for univariate time series on more than 250 data sets
  2. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  3. Multiple examples of Machine Learning forecasting with ahead
  4. rtopy (v0.1.1): calling R functions in Python
  5. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  6. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  7. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  8. 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
  9. Diffusion models in Python with esgtoolkit (Part2)
  10. Diffusion models in Python with esgtoolkit
  11. Quasi-randomized nnetworks in Julia, Python and R
  12. A plethora of datasets at your fingertips
  13. A classifier that's very accurate (and deep)
  14. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  15. Version v0.14.0 of nnetsauce for R and Python
  16. A diffusion model: G2++
  17. Diffusion models in ESGtoolkit + announcements
  18. Risk-neutralize simulations
  19. Comparing cross-validation results using crossval_ml and boxplots
  20. Did you ask ChatGPT about who you are?
  21. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  22. Simple interfaces to the forecasting API
  23. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  24. Boosted Configuration (neural) Networks Pt. 2
  25. Boosted Configuration (_neural_) Networks for classification
  26. News from ESGtoolkit, ycinterextra, and nnetsauce
  27. New version of nnetsauce -- various quasi-randomized networks
  28. A dashboard illustrating bivariate time series forecasting with `ahead`
  29. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  30. Time series cross-validation using `crossvalidation` (Part 2)
  31. Fast and scalable forecasting with ahead::ridge2f
  32. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  33. Forecasting with `ahead`
  34. `crossvalidation` and random search for calibrating support vector machines
  35. parallel grid search cross-validation using `crossvalidation`
  36. `crossvalidation` on R-universe, plus a classification example
  37. A forecasting tool (API) with examples in curl, R, Python
  38. An infinity of time series models in nnetsauce
  39. New activation functions in mlsauce's LSBoost
  40. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  41. Classify penguins with nnetsauce's MultitaskClassifier
  42. Bayesian forecasting for uni/multivariate time series
  43. Boosting nonlinear penalized least squares
  44. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  45. Submitting R package to CRAN
  46. Simulation of dependent variables in ESGtoolkit
  47. Forecasting lung disease progression
  48. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  49. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  50. nnetsauce version 0.5.0, randomized neural networks on GPU
  51. Maximizing your tip as a waiter (Part 2)
  52. Maximizing your tip as a waiter
  53. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  54. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  55. Custom errors for cross-validation using crossval::crossval_ml
  56. Encoding your categorical variables based on the response variable and correlations
  57. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  58. Grid search cross-validation using crossval
  59. Time series cross-validation using crossval
  60. On model specification, identification, degrees of freedom and regularization
  61. R notebooks for nnetsauce
  62. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  63. Feedback forms for contributing
  64. nnetsauce for R
  65. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  66. Using R in Python for statistical learning/data science
  67. Model calibration with `crossval`
  68. crossval