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

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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

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Previous publications

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