Publications by T. Moudiki
Classification using linear regression
In this post, I illustrate classification using linear regression, as implemented in Python/R package nnetsauce, and more precisely, in nnetsauce’s MultitaskClassifier. If you’re not interested in reading about the model description, you can jump directly to the 2nd section, “Two examples in Python”. In addition, the source code is relati...
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Tuning and interpreting LSBoost
There is a plethora of Automated Machine Learning tools in the wild, implementing Machine Learning (ML) pipelines from data cleaning to model validation. In this post, the input data set is already cleaned and pre-processed (diabetes dataset); the ML model is already chosen too, mlsauce’s LSBoost. We are going to focus on two important steps of...
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Forecasting with `ahead` (Python version)
A few weeks ago, I introduced the R version of ahead, a package for univariate and multivariate time series forecasting. A Python version, built on top of the R version, is now available on PyPI and GitHub. Here is how to install it:1st method: from PyPI (stable version) pip install ahead ...
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Explaining a Keras _neural_ network predictions with the-teller
Last year, in a previous post, I’ve used Python package the-teller to explain an xgboost model’s predictions. After reading today’s post, you’ll be able to use that same package, the-teller, to explain predictions of a Keras neural network trained on tabular data.We start by installing the following tools:An AutoML system based on Keras: ...
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Super Mario Bros © in the browser using PyScript
**Disclaimer: ** I have no affiliation with Anaconda Inc.If you’re a Pythonista, you’ve probably been hearing about Anaconda’s PyScript in the past few days. Since it’s been introduced at PyCon US 2022, its growth in terms of GitHub stars has been spectacular (to those who are downloading/using my packages thousands of times each month, I...
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A Machine Learning workflow using Techtonique
Contents0 – Import packages that will be used in the demo1 – Data-wrangling (using the querier)2 – Modeling/Hyperparameter tuning (using mlsauce and GPopt)3 – Explain model’s decisions (using the-teller)0 – Import packages!pip install querier # A query language for Python Data Frames (part of Techtonique) !pip install mlsauce # Miscel...
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Prediction intervals (not only) for Boosted Configuration Networks in Python
In this post, I use the following Python packages:BCN: for adjusting Boosted Configuration Networks regression (see post 1 and post 2 for more details on BCNs, and this notebook for classification examples) to sklearn’s diabetes dataset.the-teller: for interpreting BCNs, and obtaining prediction intervals. So far, as of october 2022, the-telle...
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A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
Content 0 – Intro 1 – Create an account 2 – Get a token for authentication 3 – Requests for forecasts 4 – On model calibration and cross-validation0 – IntroIn this post, I’ll describe an (work-in-progress) Application Programming Interface (API) for time series forecasting. An API is a system that can receive requests from your comp...
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Simple interfaces to the forecasting API
A few weeks ago, I introduced a forecasting API (Application Programming Interface). The application can be found here: https://techtonique2.herokuapp.com/ So far, as of 2022-11-23, this API contains four methods for univariate time series forecasting (with prediction intervals): mean a (not so naïve) benchmark method, whose prediction is the...
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Boosted Configuration (neural) Networks Pt. 2
A few weeks ago, I introduced Boosted Configuration (neural) Networks (BCNs), with some examples of classification on toy datasets. Since then, I’ve implemented BCN for regression (continuous responses) in R, and released a Python version (built on top of the R version) of the package on PyPi. What are BCNs? Statistical/Machine Learning (ML) m...
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