Publications by T. Moudiki
Back next week, and a few announcements
nnetsauce, mlsauce, the querier and the teller are now stored under Techtonique, and not on my personal GitHub account anymore. I’ll be back next week, with:1-A new, cython-ready version of nnetsauce including nonlinear Generalized Linear Models (GLM). If you want a sneak peek of this new version, you can execute the following jupyter notebook ...
1116 sym 2 img
A new version of nnetsauce, and a new Techtonique website
As a reminder nnetsauce, mlsauce, the querier and the teller are now stored under Techtonique – for those who have already starred, you can report your stars there! A new Techtonique website is also out know; it contains documentation + examples for nnetsauce, mlsauce, the querier and the teller, and is a work in progress. Figure: New Techtoniq...
2095 sym 6 img
Technical documentation
All the Python packages presented in this blog: nnetsauce Statistical/Machine Learning using Randomized and Quasi-Randomized (neural) networks | feedback form querier A query language for Python Data Frames | feedback form mlsauce Miscellaneous Statistical/Machine Learning stuff | feedback form teller Model-agnostic Statis...
1374 sym 4 img
Generalized nonlinear models in nnetsauce
This post explores a few configurations of nnetsauce’s Generalized nonlinear models (GNLMs), especially the regularization parameters guarding the model against overfitting. There are many, many other configurations that can be envisaged, which I will do over time. GNLM is still very young and experimental. There’ll be no advanced tuning in t...
3698 sym 4 img
A deeper learning architecture in nnetsauce
As you may know already (or not), nnetsauce contains CustomClassifier and CustomRegressor models, which allow to derive a new statistical/Machine Learning (ML) model from another one.Creating this new ML model is achieved by doing some feature engineering. That is, by augmenting the original set of explanatory variables, using random or quasirand...
2216 sym Python (1659 sym/7 pcs) 10 img
Explaining xgboost predictions with the teller
Nowadays, explaining the decisions of Statistical/Machine learning (ML) algorithms is becoming a must, and also, mainstream. In healthcare for example, ML explainers could help in understanding how black-box – but accurate – ML prognosis about patients are formed.One way to obtain these explanations (here is another way that I introduced in a...
3043 sym Python (1187 sym/5 pcs) 2 img
Compatibility of nnetsauce and mlsauce with scikit-learn
Disclaimer: I have no affiliation with the scikit-learn team.Thanks to inheritance, nnetsauce and mlsauce models share a lot of properties with scikit-learn’s Statistical/Machine learning (ML) models. That’s to say: if you’re already familiar with scikit-learn, you won’t have to spend a lot of time figuring out how do nnetsauce and mlsauc...
2234 sym Python (5320 sym/16 pcs) 2 img
Bayesian Optimization with GPopt
Due to the way it mixes several – relatively – simple concepts, Bayesian optimization (BO) is one of the most elegant mathematical tool I’ve encountered so far. In this post, I introduce GPopt, a tool for BO that I implemented in Python (no technical docs yet, but coming soon). The examples of GPopt’s use showcased here are based on Gaus...
2687 sym Python (3234 sym/26 pcs) 8 img
Bayesian Optimization with GPopt Part 2 (save and resume)
Two weeks ago, I presented GPopt: a Python package for Bayesian optimization. In particular, I’ve presented a way to stop the optimizer and resume it later by adding more iterations.This week, I present a way to save and resume, that makes the optimizer’s data persistent. Behind this saving feature, are hidden Python shelves which are – so...
996 sym Python (1674 sym/7 pcs) 4 img
Hyperparameters tuning with GPopt
Statistical/Machine learning models can have multiple hyperparameters that control their performance (out-of-sample accuracy, area under the curve, Root Mean Squared Error, etc.). In this post, in order to determine these hyperparameters for mlsauce’s LSBoostClassifier (on the wine dataset), cross-validation is used along with a Bayesian optimi...
1072 sym Python (2913 sym/9 pcs)