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Blogroll | Top


List of posts by date of publication | Top

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


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. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  2. Probability of receiving a loan; using learningmachine
  3. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  4. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  5. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  6. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  7. learningmachine v1.1.2: for Python
  8. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  9. rtopy (v0.1.1): calling R functions in Python
  10. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  11. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  12. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  13. Tuning Machine Learning models with GPopt's new version Part 2
  14. Tuning Machine Learning models with GPopt's new version
  15. Subsampling continuous and discrete response variables
  16. DeepMTS, a Deep Learning Model for Multivariate Time Series
  17. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  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
  19. Diffusion models in Python with esgtoolkit (Part2)
  20. Diffusion models in Python with esgtoolkit
  21. Quasi-randomized nnetworks in Julia, Python and R
  22. A plethora of datasets at your fingertips
  23. A classifier that's very accurate (and deep)
  24. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  25. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  26. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  27. Version v0.14.0 of nnetsauce for R and Python
  28. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  29. (News from) forecasting in Python with ahead (progress bars and plots)
  30. Forecasting in Python with ahead
  31. Did you ask ChatGPT about who you are?
  32. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  33. Simple interfaces to the forecasting API
  34. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  35. Prediction intervals (not only) for Boosted Configuration Networks in Python
  36. A Machine Learning workflow using Techtonique
  37. Super Mario Bros © in the browser using PyScript
  38. News from ESGtoolkit, ycinterextra, and nnetsauce
  39. Explaining a Keras _neural_ network predictions with the-teller
  40. New version of nnetsauce -- various quasi-randomized networks
  41. A dashboard illustrating bivariate time series forecasting with `ahead`
  42. Forecasting with `ahead` (Python version)
  43. Tuning and interpreting LSBoost
  44. Classification using linear regression
  45. Documentation and source code for GPopt, a package for Bayesian optimization
  46. Hyperparameters tuning with GPopt
  47. A forecasting tool (API) with examples in curl, R, Python
  48. Bayesian Optimization with GPopt Part 2 (save and resume)
  49. Bayesian Optimization with GPopt
  50. Compatibility of nnetsauce and mlsauce with scikit-learn
  51. Explaining xgboost predictions with the teller
  52. An infinity of time series models in nnetsauce
  53. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  54. A deeper learning architecture in nnetsauce
  55. Generalized nonlinear models in nnetsauce
  56. Boosting nonlinear penalized least squares
  57. Technical documentation
  58. A new version of nnetsauce, and a new Techtonique website
  59. Back next week, and a few announcements
  60. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  61. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  62. nnetsauce version 0.5.0, randomized neural networks on GPU
  63. Maximizing your tip as a waiter (Part 2)
  64. Parallel AdaOpt classification
  65. Maximizing your tip as a waiter
  66. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  67. AdaOpt
  68. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  69. Encoding your categorical variables based on the response variable and correlations
  70. Documentation for the querier, a query language for Data Frames
  71. Import data into the querier (now on Pypi), a query language for Data Frames
  72. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  73. A new version of nnetsauce (v0.3.1)
  74. Using R in Python for statistical learning/data science
  75. 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. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  2. Probability of receiving a loan; using learningmachine
  3. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  4. Conformalized predictive simulations for univariate time series on more than 250 data sets
  5. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  6. Multiple examples of Machine Learning forecasting with ahead
  7. rtopy (v0.1.1): calling R functions in Python
  8. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  9. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  10. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  11. 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
  12. Diffusion models in Python with esgtoolkit (Part2)
  13. Diffusion models in Python with esgtoolkit
  14. Quasi-randomized nnetworks in Julia, Python and R
  15. A plethora of datasets at your fingertips
  16. A classifier that's very accurate (and deep)
  17. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  18. Version v0.14.0 of nnetsauce for R and Python
  19. A diffusion model: G2++
  20. Diffusion models in ESGtoolkit + announcements
  21. Risk-neutralize simulations
  22. Comparing cross-validation results using crossval_ml and boxplots
  23. Did you ask ChatGPT about who you are?
  24. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  25. Simple interfaces to the forecasting API
  26. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  27. Boosted Configuration (neural) Networks Pt. 2
  28. Boosted Configuration (_neural_) Networks for classification
  29. News from ESGtoolkit, ycinterextra, and nnetsauce
  30. New version of nnetsauce -- various quasi-randomized networks
  31. A dashboard illustrating bivariate time series forecasting with `ahead`
  32. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  33. Time series cross-validation using `crossvalidation` (Part 2)
  34. Fast and scalable forecasting with ahead::ridge2f
  35. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  36. Forecasting with `ahead`
  37. `crossvalidation` and random search for calibrating support vector machines
  38. parallel grid search cross-validation using `crossvalidation`
  39. `crossvalidation` on R-universe, plus a classification example
  40. A forecasting tool (API) with examples in curl, R, Python
  41. An infinity of time series models in nnetsauce
  42. New activation functions in mlsauce's LSBoost
  43. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  44. Classify penguins with nnetsauce's MultitaskClassifier
  45. Bayesian forecasting for uni/multivariate time series
  46. Boosting nonlinear penalized least squares
  47. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  48. Submitting R package to CRAN
  49. Simulation of dependent variables in ESGtoolkit
  50. Forecasting lung disease progression
  51. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  52. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  53. nnetsauce version 0.5.0, randomized neural networks on GPU
  54. Maximizing your tip as a waiter (Part 2)
  55. Maximizing your tip as a waiter
  56. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  57. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  58. Custom errors for cross-validation using crossval::crossval_ml
  59. Encoding your categorical variables based on the response variable and correlations
  60. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  61. Grid search cross-validation using crossval
  62. Time series cross-validation using crossval
  63. On model specification, identification, degrees of freedom and regularization
  64. R notebooks for nnetsauce
  65. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  66. Feedback forms for contributing
  67. nnetsauce for R
  68. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  69. Using R in Python for statistical learning/data science
  70. Model calibration with `crossval`
  71. crossval