Change in blog's presentation

04 Sep 2019 | RSS | Back to list of posts | Toggle dark mode | Hire me on Malt | Hire me on Fiverr | Hire me on Upwork

Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user, no matter the pricing tier.

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I used to have a monolithic html page for blog posts at /blog, but it was neither practical nor sustainable.

I am now using Jekyll, a tool that allows me to separate posts in different Markdown files. For those who are interested in learning more about Jekyll, this blog post was helpful to me.

As a consequence, for those who used the previous version of this blog, you now have:

• A main page, listing all the posts, sorted by date of publication
• When you click on a link on that page, you have access to the requested post’s page

Comments powered by Talkyard.

Under License Creative Commons Attribution 4.0 International

Previous publications

1. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out May 28, 2025
2. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
3. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
4. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
5. Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
6. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
7. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
8. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
9. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series) Apr 14, 2025
10. Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
11. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
12. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
13. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
14. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
15. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
16. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
17. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
18. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
19. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
20. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
21. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
22. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
23. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart Feb 10, 2025
24. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
25. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
26. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
27. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition Jan 20, 2025
28. Techtonique is out! (with a tutorial in various programming languages and formats) Jan 14, 2025
29. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
30. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Jan 5, 2025
31. Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 31, 2024
32. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
33. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
34. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
35. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
36. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
37. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
38. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
39. GLMNet in Python: Generalized Linear Models Nov 18, 2024
40. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
41. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
42. Chat with your tabular data in www.techtonique.net Oct 30, 2024
43. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
44. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
45. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
46. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
47. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
48. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
49. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
50. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
51. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
52. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
53. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
54. New versions of nnetsauce and ahead Sep 9, 2024
55. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
56. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
57. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
58. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
59. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
60. Bayesian (nonlinear) adaptive learning Aug 12, 2024
61. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
62. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
63. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
64. learningmachine for Python (new version) Jul 15, 2024
65. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
66. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
67. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
68. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
69. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
70. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
71. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
72. Forecasting the Economy May 27, 2024
73. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
74. Probability of receiving a loan; using learningmachine May 12, 2024
75. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
76. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
77. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
78. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
79. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
80. learningmachine v1.1.2: for Python Apr 1, 2024
81. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
82. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
83. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
84. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
85. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
86. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
87. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
88. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
89. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
90. Subsampling continuous and discrete response variables Jan 22, 2024
91. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
92. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
93. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
94. 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
95. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
96. Diffusion models in Python with esgtoolkit Dec 11, 2023
97. Julia packaging at the command line Dec 4, 2023
98. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
99. A plethora of datasets at your fingertips Nov 20, 2023
100. A classifier that's very accurate (and deep) Nov 12, 2023
101. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
102. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
103. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
104. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
105. A diffusion model: G2++ Oct 9, 2023
106. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
107. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
108. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
109. Forecasting in Python with ahead Sep 11, 2023
110. Risk-neutralize simulations Sep 4, 2023
111. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
112. Reminder Apr 30, 2023
113. Did you ask ChatGPT about who you are? Apr 16, 2023
114. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
115. Simple interfaces to the forecasting API Nov 23, 2022
116. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
117. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
118. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
119. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
120. A Machine Learning workflow using Techtonique Jun 6, 2022
121. Super Mario Bros © in the browser using PyScript May 8, 2022
122. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
123. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
124. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
125. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
126. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
127. Forecasting with `ahead` (Python version) Dec 13, 2021
128. Tuning and interpreting LSBoost Nov 15, 2021
129. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
130. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
131. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
132. Forecasting with `ahead` Oct 15, 2021
133. Classification using linear regression Sep 26, 2021
134. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
135. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
136. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
137. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
138. Hyperparameters tuning with GPopt Jun 11, 2021
139. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
140. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
141. Bayesian Optimization with GPopt Apr 16, 2021
142. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
143. Explaining xgboost predictions with the teller Mar 12, 2021
144. An infinity of time series models in nnetsauce Mar 6, 2021
145. New activation functions in mlsauce's LSBoost Feb 12, 2021
146. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
147. A deeper learning architecture in nnetsauce Dec 18, 2020
148. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
149. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
150. Generalized nonlinear models in nnetsauce Nov 28, 2020
151. Boosting nonlinear penalized least squares Nov 21, 2020
152. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
153. NEWS Oct 30, 2020
154. A glimpse into my PhD journey Oct 23, 2020
155. Submitting R package to CRAN Oct 16, 2020
156. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
157. Forecasting lung disease progression Oct 2, 2020
158. New nnetsauce Sep 25, 2020
159. Technical documentation Sep 18, 2020
160. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
161. Back next week, and a few announcements Sep 4, 2020
162. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
163. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
164. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
165. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
166. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
167. Announcements Jun 26, 2020
168. Parallel AdaOpt classification Jun 19, 2020
169. Comments section and other news Jun 12, 2020
170. Maximizing your tip as a waiter Jun 5, 2020
171. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
172. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
173. AdaOpt May 15, 2020
174. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
175. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
176. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
177. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
178. Grid search cross-validation using crossval Apr 10, 2020
179. Documentation for the querier, a query language for Data Frames Apr 3, 2020
180. Time series cross-validation using crossval Mar 27, 2020
181. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
182. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
183. R notebooks for nnetsauce Mar 6, 2020
184. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
185. Create a specific feed in your Jekyll blog Feb 21, 2020
186. Git/Github for contributing to package development Feb 14, 2020
187. Feedback forms for contributing Feb 7, 2020
188. nnetsauce for R Jan 31, 2020
189. A new version of nnetsauce (v0.3.1) Jan 24, 2020
190. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
191. Search bar, new year 2020 Jan 10, 2020
192. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
193. Understanding model interactions with the `teller` Dec 13, 2019
194. Using the `teller` on a classifier Dec 6, 2019
195. Benchmarking the querier's verbs Nov 29, 2019
196. Composing the querier's verbs for data wrangling Nov 22, 2019
197. Comparing and explaining model predictions with the teller Nov 15, 2019
198. Tests for the significance of marginal effects in the teller Nov 8, 2019
199. Introducing the teller Nov 1, 2019
200. Introducing the querier Oct 25, 2019
201. Prediction intervals for nnetsauce models Oct 18, 2019
202. Using R in Python for statistical learning/data science Oct 11, 2019
203. Model calibration with `crossval` Oct 4, 2019
204. Bagging in the nnetsauce Sep 25, 2019
205. Adaboost learning with nnetsauce Sep 18, 2019
206. Change in blog's presentation Sep 4, 2019
207. nnetsauce on Pypi Jun 5, 2019
208. More nnetsauce (examples of use) May 9, 2019
209. nnetsauce Mar 13, 2019
210. crossval Mar 13, 2019
211. test Mar 10, 2019