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Blogroll | Top

List of posts by date of publication | Top

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

QuasiRandomizedNN

  1. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  2. 10 uncertainty quantification methods in nnetsauce forecasting
  3. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  4. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  5. Automated hyperparameter tuning using any conformalized surrogate
  6. Recognizing handwritten digits with Ridge2Classifier
  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. DeepMTS, a Deep Learning Model for Multivariate Time Series
  10. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  11. Quasi-randomized nnetworks in Julia, Python and R
  12. A classifier that's very accurate (and deep)
  13. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  14. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  15. Version v0.14.0 of nnetsauce for R and Python
  16. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  17. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  18. New version of nnetsauce -- various quasi-randomized networks
  19. Tuning and interpreting LSBoost
  20. Classification using linear regression
  21. An infinity of time series models in nnetsauce
  22. A deeper learning architecture in nnetsauce
  23. Classify penguins with nnetsauce's MultitaskClassifier
  24. Bayesian forecasting for uni/multivariate time series
  25. New nnetsauce
  26. Technical documentation
  27. A new version of nnetsauce, and a new Techtonique website
  28. Back next week, and a few announcements
  29. nnetsauce version 0.5.0, randomized neural networks on GPU
  30. R notebooks for nnetsauce
  31. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  32. Feedback forms for contributing
  33. nnetsauce for R
  34. A new version of nnetsauce (v0.3.1)
  35. 2019 Recap, the nnetsauce, the teller and the querier
  36. Prediction intervals for nnetsauce models
  37. Bagging in the nnetsauce
  38. Adaboost learning with nnetsauce
  39. nnetsauce on Pypi
  40. More nnetsauce (examples of use)
  41. nnetsauce

R

  1. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  2. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  3. Forecasting the Economy
  4. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  5. Probability of receiving a loan; using learningmachine
  6. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  7. Conformalized predictive simulations for univariate time series on more than 250 data sets
  8. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  9. Multiple examples of Machine Learning forecasting with ahead
  10. rtopy (v0.1.1): calling R functions in Python
  11. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  12. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  13. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  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. Version v0.14.0 of nnetsauce for R and Python
  22. A diffusion model: G2++
  23. Diffusion models in ESGtoolkit + announcements
  24. Risk-neutralize simulations
  25. Comparing cross-validation results using crossval_ml and boxplots
  26. Did you ask ChatGPT about who you are?
  27. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  28. Simple interfaces to the forecasting API
  29. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  30. Boosted Configuration (neural) Networks Pt. 2
  31. Boosted Configuration (_neural_) Networks for classification
  32. News from ESGtoolkit, ycinterextra, and nnetsauce
  33. New version of nnetsauce -- various quasi-randomized networks
  34. A dashboard illustrating bivariate time series forecasting with `ahead`
  35. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  36. Time series cross-validation using `crossvalidation` (Part 2)
  37. Fast and scalable forecasting with ahead::ridge2f
  38. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  39. Forecasting with `ahead`
  40. `crossvalidation` and random search for calibrating support vector machines
  41. parallel grid search cross-validation using `crossvalidation`
  42. `crossvalidation` on R-universe, plus a classification example
  43. A forecasting tool (API) with examples in curl, R, Python
  44. An infinity of time series models in nnetsauce
  45. New activation functions in mlsauce's LSBoost
  46. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  47. Classify penguins with nnetsauce's MultitaskClassifier
  48. Bayesian forecasting for uni/multivariate time series
  49. Boosting nonlinear penalized least squares
  50. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  51. Submitting R package to CRAN
  52. Simulation of dependent variables in ESGtoolkit
  53. Forecasting lung disease progression
  54. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  55. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  56. nnetsauce version 0.5.0, randomized neural networks on GPU
  57. Maximizing your tip as a waiter (Part 2)
  58. Maximizing your tip as a waiter
  59. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  60. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  61. Custom errors for cross-validation using crossval::crossval_ml
  62. Encoding your categorical variables based on the response variable and correlations
  63. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  64. Grid search cross-validation using crossval
  65. Time series cross-validation using crossval
  66. On model specification, identification, degrees of freedom and regularization
  67. R notebooks for nnetsauce
  68. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  69. Feedback forms for contributing
  70. nnetsauce for R
  71. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  72. Using R in Python for statistical learning/data science
  73. Model calibration with `crossval`
  74. crossval