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10 Mar 2019 | RSS | Back to list of posts

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Previous publications

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
Super Mario Bros © in the browser using PyScript May 8, 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