Conformal Optimization beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets
Link to the notebook at then of the post:
thierrymoudiki.github.io/blog/2026/04...
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Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
thierrymoudiki.github.io/blog/2025/09/09/r/python...
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Technical documentation
thierrymoudiki.github.io/blog/2020/09/18/misc/pyt...
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Quasi-randomized nnetworks in Julia, Python and R
thierrymoudiki.github.io/blog/2023/11/27/python/r...
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Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
thierrymoudiki.github.io/blog/2024/06/17/python/q...
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Back next week, and a few announcements
thierrymoudiki.github.io/blog/2020/09/04/misc/pyt...
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R package development workflow (assuming you're using macOS or Linux)
thierrymoudiki.github.io/blog/2024/08/27/r/makefi...
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Explaining a Keras _neural_ network predictions with the-teller
thierrymoudiki.github.io/blog/2022/03/11/python/e...
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A new version of nnetsauce (v0.3.1)
thierrymoudiki.github.io/blog/2020/01/24/python/q...
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Bayesian Optimization with GPopt
thierrymoudiki.github.io/blog/2021/04/16/python/m...
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Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
thierrymoudiki.github.io/blog/2024/09/02/python/r...
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mlS3 — A Unified S3 Machine Learning Interface in R
Key Design Principle
All models follow the same two-step workflow:
mod <- wrap_*(X_train, y_train, ...) # Train
pred <- predict(mod, newx = X_test, ...) # Predict
thierrymoudiki.github.io/blog/2026/04...
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New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
thierrymoudiki.github.io/blog/2020/07/03/misc/mls...
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(News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
thierrymoudiki.github.io/blog/2025/03/09/r/ridge2...
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Forecasting lung disease progression
thierrymoudiki.github.io/blog/2020/10/02/misc/r/o...
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Real-time pricing with a pretrained probabilistic stock return model
thierrymoudiki.github.io/blog/2025/10/01/r/python...
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You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
thierrymoudiki.github.io/blog/2024/12/01/python/n...
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Generalized nonlinear models in nnetsauce
thierrymoudiki.github.io/blog/2020/11/28/explaina...
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How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
thierrymoudiki.github.io/blog/2025/05/27/r/python...
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Encoding your categorical variables based on the response variable and correlations
thierrymoudiki.github.io/blog/2020/04/24/python/r...
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Composing the querier's verbs for data wrangling
thierrymoudiki.github.io/blog/2019/11/22/database...
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Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
thierrymoudiki.github.io/blog/2025/03/08/r/python...
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New version of nnetsauce -- various quasi-randomized networks
thierrymoudiki.github.io/blog/2022/02/12/r/python...
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A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
thierrymoudiki.github.io/blog/2022/11/02/python/r...
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Did you ask ChatGPT about who you are?
thierrymoudiki.github.io/blog/2023/04/16/python/r...
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Bayesian forecasting for uni/multivariate time series
thierrymoudiki.github.io/blog/2020/12/04/r/quasir...
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or something else?
"the architecture alone wouldn't be considered XAI": you mean, the package would need to integrate a way to derive those gradients? Plus, integrated gradients?
It's flexible (can use any sklearn-like base learner), nonlinear (based on quasi-randomized NN) and fast.
Interested in seeing any application