Subsampling continuous and discrete response variables

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New in nnetsauce v0.16.3:

• add robust scaler to type_scaling
• relatively faster scaling in preprocessing
• Regression-based classifiers (see https://www.researchgate.net/publication/377227280_Regression-based_machine_learning_classifiers)
• DeepMTS (multivariate time series forecasting with deep quasi-random layers): see https://thierrymoudiki.github.io/blog/2024/01/15/python/quasirandomizednn/forecasting/DeepMTS
• AutoML for MTS (multivariate time series forecasting): see https://thierrymoudiki.github.io/blog/2023/10/29/python/quasirandomizednn/MTS-LazyPredict
• AutoML for DeepMTS (multivariate time series forecasting): see https://github.com/Techtonique/nnetsauce/blob/master/nnetsauce/demo/thierrymoudiki_20240106_LazyDeepMTS.ipynb
• Spaghetti plots for MTS and DeepMTS (multivariate time series forecasting): see below
• Subsample continuous and discrete responses

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Previous publications

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