NEWS

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

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Next week, I’ll be presenting and discussing Statistical/Machine Learning explainability using a particular surrogate.

The week after, it will be about explainable ‘AI’ using Gradient Boosted randomized networks. Again!

And the week after: a deeper dive into Generalized (Non)linear models (GNLM) for continuous and discrete outputs in nnetsauce. For a refresher, you can play with this notebook containing examples of GNLMs. Issues/Pull requests, suggestions are welcome, as usual.

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Previous publications

survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
GLMNet in Python: Generalized Linear Models Nov 18, 2024
Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
Chat with your tabular data in www.techtonique.net Oct 30, 2024
Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
Gradient-Boosting anything (alert: high performance) Oct 6, 2024
Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
New versions of nnetsauce and ahead Sep 9, 2024
Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
Bayesian (nonlinear) adaptive learning Aug 12, 2024
Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
learningmachine for Python (new version) Jul 15, 2024
learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
Forecasting the Economy May 27, 2024
A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
Probability of receiving a loan; using learningmachine May 12, 2024
mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
learningmachine v1.1.2: for Python Apr 1, 2024
learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
Tuning Machine Learning models with GPopt's new version Jan 29, 2024
Subsampling continuous and discrete response variables Jan 22, 2024
DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
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
Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
Diffusion models in Python with esgtoolkit Dec 11, 2023
Julia packaging at the command line Dec 4, 2023
Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
A plethora of datasets at your fingertips Nov 20, 2023
A classifier that's very accurate (and deep) Nov 12, 2023
mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
A diffusion model: G2++ Oct 9, 2023
Diffusion models in ESGtoolkit + announcements Oct 2, 2023
An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
(News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
Forecasting in Python with ahead Sep 11, 2023
Risk-neutralize simulations Sep 4, 2023
Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
Reminder Apr 30, 2023
Did you ask ChatGPT about who you are? Apr 16, 2023
A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
Simple interfaces to the forecasting API Nov 23, 2022
A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
A Machine Learning workflow using Techtonique Jun 6, 2022
News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
Forecasting with `ahead` (Python version) Dec 13, 2021
Tuning and interpreting LSBoost Nov 15, 2021
Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
Forecasting with `ahead` Oct 15, 2021
Classification using linear regression Sep 26, 2021
`crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
`crossvalidation` on R-universe, plus a classification example Jul 23, 2021
Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
Hyperparameters tuning with GPopt Jun 11, 2021
A forecasting tool (API) with examples in curl, R, Python May 28, 2021
Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
Bayesian Optimization with GPopt Apr 16, 2021
Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
Explaining xgboost predictions with the teller Mar 12, 2021
An infinity of time series models in nnetsauce Mar 6, 2021
New activation functions in mlsauce's LSBoost Feb 12, 2021
2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
A deeper learning architecture in nnetsauce Dec 18, 2020
Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
Bayesian forecasting for uni/multivariate time series Dec 4, 2020
Generalized nonlinear models in nnetsauce Nov 28, 2020
Boosting nonlinear penalized least squares Nov 21, 2020
Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
NEWS Oct 30, 2020
A glimpse into my PhD journey Oct 23, 2020
Submitting R package to CRAN Oct 16, 2020
Simulation of dependent variables in ESGtoolkit Oct 9, 2020
Forecasting lung disease progression Oct 2, 2020
New nnetsauce Sep 25, 2020
Technical documentation Sep 18, 2020
A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
Back next week, and a few announcements Sep 4, 2020
Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
Maximizing your tip as a waiter (Part 2) Jul 10, 2020
New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
Announcements Jun 26, 2020
Parallel AdaOpt classification Jun 19, 2020
Comments section and other news Jun 12, 2020
Maximizing your tip as a waiter Jun 5, 2020
AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
AdaOpt May 15, 2020
Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
Grid search cross-validation using crossval Apr 10, 2020
Documentation for the querier, a query language for Data Frames Apr 3, 2020
Time series cross-validation using crossval Mar 27, 2020
On model specification, identification, degrees of freedom and regularization Mar 20, 2020
Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
R notebooks for nnetsauce Mar 6, 2020
Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
Create a specific feed in your Jekyll blog Feb 21, 2020
Git/Github for contributing to package development Feb 14, 2020
Feedback forms for contributing Feb 7, 2020
nnetsauce for R Jan 31, 2020
A new version of nnetsauce (v0.3.1) Jan 24, 2020
ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
Search bar, new year 2020 Jan 10, 2020
2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
Understanding model interactions with the `teller` Dec 13, 2019
Using the `teller` on a classifier Dec 6, 2019
Benchmarking the querier's verbs Nov 29, 2019
Composing the querier's verbs for data wrangling Nov 22, 2019
Comparing and explaining model predictions with the teller Nov 15, 2019
Tests for the significance of marginal effects in the teller Nov 8, 2019
Introducing the teller Nov 1, 2019
Introducing the querier Oct 25, 2019
Prediction intervals for nnetsauce models Oct 18, 2019
Using R in Python for statistical learning/data science Oct 11, 2019
Model calibration with `crossval` Oct 4, 2019
Bagging in the nnetsauce Sep 25, 2019
Adaboost learning with nnetsauce Sep 18, 2019
Change in blog's presentation Sep 4, 2019
nnetsauce on Pypi Jun 5, 2019
More nnetsauce (examples of use) May 9, 2019
nnetsauce Mar 13, 2019
crossval Mar 13, 2019
test Mar 10, 2019